Agnostically Learning Single-Index Models using Omnipredictors

We give the first result for agnostically learning Single-Index Models (SIMs) with arbitrary monotone and Lipschitz activations. All prior work either held only in the realizable setting or required the activation to be known. Moreover, we only require the marginal to have bounded second moments, whereas all prior work required stronger distributional assumptions (such as anticoncentration or boundedness). Our algorithm is based on recent work by [GHK+23] on omniprediction using predictors satisfying calibrated multiaccuracy. Our analysis is simple and relies on the relationship between…Apple Machine Learning Research

Flag harmful content using Amazon Comprehend toxicity detection

Flag harmful content using Amazon Comprehend toxicity detection

Online communities are driving user engagement across industries like gaming, social media, ecommerce, dating, and e-learning. Members of these online communities trust platform owners to provide a safe and inclusive environment where they can freely consume content and contribute. Content moderators are often employed to review user-generated content and check that it’s safe and compliant with your terms of use. However, the ever-increasing scale, complexity, and variety of inappropriate content makes human moderation workflows unscalable and expensive. The result is poor, harmful, and non-inclusive communities that disengage users and negatively impact the community and business.

Along with user-generated content, machine-generated content has brought a fresh challenge to content moderation. It automatically creates highly realistic content that may be inappropriate or harmful at scale. The industry is facing the new challenge of automatically moderating content generated by AI to protect users from harmful material.

In this post, we introduce toxicity detection, a new feature from Amazon Comprehend that helps you automatically detect harmful content in user- or machine-generated text. This includes plain text, text extracted from images, and text transcribed from audio or video content.

Detect toxicity in text content with Amazon Comprehend

Amazon Comprehend is a natural-language processing (NLP) service that uses machine learning (ML) to uncover valuable insights and connections in text. It offers a range of ML models that can be either pre-trained or customized through API interfaces. Amazon Comprehend now provides a straightforward, NLP-based solution for toxic content detection in text.

The Amazon Comprehend Toxicity Detection API assigns an overall toxicity score to text content, ranging from 0–1, indicating the likelihood of it being toxic. It also categorizes text into the following seven categories and provides a confidence score for each:

  • HATE_SPEECH – Speech that criticizes, insults, denounces, or dehumanizes a person or a group on the basis of an identity, be it race, ethnicity, gender identity, religion, sexual orientation, ability, national origin, or another identity group.
  • GRAPHIC – Speech that uses visually descriptive, detailed, and unpleasantly vivid imagery. Such language is often made verbose so as to amplify an insult, or discomfort or harm to the recipient.
  • HARASSMENT_OR_ABUSE – Speech that imposes disruptive power dynamics between the speaker and hearer (regardless of intent), seeks to affect the psychological well-being of the recipient, or objectifies a person.
  • SEXUAL – Speech that indicates sexual interest, activity, or arousal by using direct or indirect references to body parts, physical traits, or sex.
  • VIOLENCE_OR_THREAT – Speech that includes threats that seek to inflict pain, injury, or hostility towards a person or group.
  • INSULT – Speech that includes demeaning, humiliating, mocking, insulting, or belittling language.
  • PROFANITY – Speech that contains words, phrases, or acronyms that are impolite, vulgar, or offensive.

You can access the Toxicity Detection API by calling it directly using the AWS Command Line Interface (AWS CLI) and AWS SDKs. Toxicity detection in Amazon Comprehend is currently supported in the English language.

Use cases

Text moderation plays a crucial role in managing user-generated content across diverse formats, including social media posts, online chat messages, forum discussions, website comments, and more. Moreover, platforms that accept video and audio content can use this feature to moderate transcribed audio content.

The emergence of generative AI and large language models (LLMs) represents the latest trend in the field of AI. Consequently, there is a growing need for responsive solutions to moderate content generated by LLMs. The Amazon Comprehend Toxicity Detection API is ideally suited for addressing this need.

Amazon Comprehend Toxicity Detection API request

You can send up to 10 text segments to the Toxicity Detection API, each with a size limit of 1 KB. Every text segment in the request is handled independently. In the following example, we generate a JSON file named toxicity_api_input.json containing the text content, including three sample text segments for moderation. Note that in the example, the profane words are masked as XXXX.

{
  "TextSegments": [     
    {"Text": "and go through the door go through the door he's on the right"},
    {"Text": "he's on the right XXXXX him"},
    {"Text": "what the XXXX are you doing man that's why i didn't want to play"}
  ],
  "LanguageCode": "en"
}

You can use the AWS CLI to invoke the Toxicity Detection API using the preceding JSON file containing the text content:

aws comprehend detect-toxic-content --cli-input-json file://toxicity_api_input.json

Amazon Comprehend Toxicity Detection API response

The Toxicity Detection API response JSON output will include the toxicity analysis result in the ResultList field. ResultList lists the text segment items, and the sequence represents the order in which the text sequences were received in the API request. Toxicity represents the overall confidence score of detection (between 0–1). Labels includes a list of toxicity labels with confidence scores, categorized by the type of toxicity.

The following code shows the JSON response from the Toxicity Detection API based on the request example in the previous section:

{
    "ResultList": [
        {
            "Toxicity": 0.009200000204145908,
            "Labels": [
                { "Name": "PROFANITY", "Score": 0.0007999999797903001},
                { "Name": "HATE_SPEECH", "Score": 0.0017999999690800905},
                { "Name": "INSULT", "Score": 0.003000000026077032},
                { "Name": "GRAPHIC", "Score": 0.0010000000474974513},
                { "Name": "HARASSMENT_OR_ABUSE", "Score": 0.0013000000035390258},
                { "Name": "SEXUAL", "Score": 0.0017000000225380063},
                { "Name": "VIOLENCE_OR_THREAT", "Score": 0.004999999888241291}
            ]
        },
        {
            "Toxicity": 0.7358999848365784,
            "Labels": [
                { "Name": "PROFANITY", "Score": 0.011900000274181366},
                { "Name": "HATE_SPEECH", "Score": 0.019500000402331352},
                { "Name": "INSULT", "Score": 0.0714000016450882},
                { "Name": "GRAPHIC", "Score": 0.006099999882280827},
                { "Name": "HARASSMENT_OR_ABUSE", "Score": 0.018200000748038292},
                { "Name": "SEXUAL", "Score": 0.0027000000700354576},
                { "Name": "VIOLENCE_OR_THREAT", "Score": 0.8145999908447266}
            ]
        },
        {
            "Toxicity": 0.9843000173568726,
            "Labels": [
                { "Name": "PROFANITY", "Score": 0.9369999766349792 },
                { "Name": "HATE_SPEECH", "Score": 0.30880001187324524 },
                { "Name": "INSULT", "Score": 0.42100000381469727 },
                { "Name": "GRAPHIC", "Score": 0.12630000710487366 },
                { "Name": "HARASSMENT_OR_ABUSE", "Score": 0.25519999861717224 },
                { "Name": "SEXUAL", "Score": 0.19169999659061432 },
                { "Name": "VIOLENCE_OR_THREAT", "Score": 0.19539999961853027 }
            ]
        }
    ]
}

In the preceding JSON, the first text segment is considered safe with a low toxicity score. However, the second and third text segments received toxicity scores of 73% and 98%, respectively. For the second segment, Amazon Comprehend detects a high toxicity score for VIOLENCE_OR_THREAT; for the third segment, it detects PROFANITY with a high toxicity score.

Sample request using the Python SDK

The following code snippet demonstrates how to utilize the Python SDK to invoke the Toxicity Detection API. This code receives the same JSON response as the AWS CLI command demonstrated earlier.

import boto3 import base64
# Initialize a Comprehend boto3 client object
comprehend_client = session.client('comprehend')

# Call comprehend Detect Toxic Content API with text segments
response = comprehend_client.detect_toxic_content(
    TextSegments=[
        {"Text":  "and go through the door go through the door he's on the right"},
        {"Text":  "he's on the right XXXXX him"},
        {"Text":  "what the XXXX are you doing man that's why i didn't want to play"}
    ],
    LanguageCode='en'
)

Summary

In this post, we provided an overview of the new Amazon Comprehend Toxicity Detection API. We also described how you can parse the API response JSON. For more information, refer to Comprehend API document.

Amazon Comprehend toxicity detection is now generally available in four Regions: us-east-1, us-west-2, eu-west-1, and ap-southeast-2.

To learn more about content moderation, refer to Guidance for Content Moderation on AWS. Take the first step towards streamlining your content moderation operations with AWS.

About the Authors

Author - Lana ZhangLana Zhang is a Senior Solutions Architect at AWS WWSO AI Services team, specializing in AI and ML for Content Moderation, Computer Vision, Natural Language Processing and Generative AI. With her expertise, she is dedicated to promoting AWS AI/ML solutions and assisting customers in transforming their business solutions across diverse industries, including social media, gaming, e-commerce, media, advertising & marketing.

Author - Ravisha SKRavisha SK is a Senior Product Manager, Technical at AWS with a focus on AI/ML. She has over 10 years of experience in data analytics and machine learning across different domains. In her spare time, she enjoys reading, experimenting in the kitchen and exploring new coffee shops.

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Scaling multimodal understanding to long videos

Scaling multimodal understanding to long videos

Posted by Isaac Noble, Software Engineer, Google Research, and Anelia Angelova, Research Scientist, Google DeepMind

When building machine learning models for real-life applications, we need to consider inputs from multiple modalities in order to capture various aspects of the world around us. For example, audio, video, and text all provide varied and complementary information about a visual input. However, building multimodal models is challenging due to the heterogeneity of the modalities. Some of the modalities might be well synchronized in time (e.g., audio, video) but not aligned with text. Furthermore, the large volume of data in video and audio signals is much larger than that in text, so when combining them in multimodal models, video and audio often cannot be fully consumed and need to be disproportionately compressed. This problem is exacerbated for longer video inputs.

In “Mirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities”, we introduce a multimodal autoregressive model (Mirasol3B) for learning across audio, video, and text modalities. The main idea is to decouple the multimodal modeling into separate focused autoregressive models, processing the inputs according to the characteristics of the modalities. Our model consists of an autoregressive component for the time-synchronized modalities (audio and video) and a separate autoregressive component for modalities that are not necessarily time-aligned but are still sequential, e.g., text inputs, such as a title or description. Additionally, the time-aligned modalities are partitioned in time where local features can be jointly learned. In this way, audio-video inputs are modeled in time and are allocated comparatively more parameters than prior works. With this approach, we can effortlessly handle much longer videos (e.g., 128-512 frames) compared to other multimodal models. At 3B parameters, Mirasol3B is compact compared to prior Flamingo (80B) and PaLI-X (55B) models. Finally, Mirasol3B outperforms the state-of-the-art approaches on video question answering (video QA), long video QA, and audio-video-text benchmarks.

The Mirasol3B architecture consists of an autoregressive model for the time-aligned modalities (audio and video), which are partitioned in chunks, and a separate autoregressive model for the unaligned context modalities (e.g., text). Joint feature learning is conducted by the Combiner, which learns compact but sufficiently informative features, allowing the processing of long video/audio inputs.

Coordinating time-aligned and contextual modalities

Video, audio and text are diverse modalities with distinct characteristics. For example, video is a spatio-temporal visual signal with 30–100 frames per second, but due to the large volume of data, typically only 32–64 frames per video are consumed by current models. Audio is a one-dimensional temporal signal obtained at much higher frequency than video (e.g., at 16 Hz), whereas text inputs that apply to the whole video, are typically 200–300 word-sequence and serve as a context to the audio-video inputs. To that end, we propose a model consisting of an autoregressive component that fuses and jointly learns the time-aligned signals, which occur at high frequencies and are roughly synchronized, and another autoregressive component for processing non-aligned signals. Learning between the components for the time-aligned and contextual modalities is coordinated via cross-attention mechanisms that allow the two to exchange information while learning in a sequence without having to synchronize them in time.

Time-aligned autoregressive modeling of video and audio

Long videos can convey rich information and activities happening in a sequence. However, present models approach video modeling by extracting all the information at once, without sufficient temporal information. To address this, we apply an autoregressive modeling strategy where we condition jointly learned video and audio representations for one time interval on feature representations from previous time intervals. This preserves temporal information.

The video is first partitioned into smaller video chunks. Each chunk itself can be 4–64 frames. The features corresponding to each chunk are then processed by a learning module, called the Combiner (described below), which generates a joint audio and video feature representation at the current step — this step extracts and compacts the most important information per chunk. Next, we process this joint feature representation with an autoregressive Transformer, which applies attention to the previous feature representation and generates the joint feature representation for the next step. Consequently, the model learns how to represent not only each individual chunk, but also how the chunks relate temporally.

We use an autoregressive modeling of the audio and video inputs, partitioning them in time and learning joint feature representations, which are then autoregressively learned in sequence.

Modeling long videos with a modality combiner

To combine the signals from the video and audio information in each video chunk, we propose a learning module called the Combiner. Video and audio signals are aligned by taking the audio inputs that correspond to a specific video timeframe. We then process video and audio inputs spatio-temporally, extracting information particularly relevant to changes in the inputs (for videos we use sparse video tubes, and for audio we apply the spectrogram representation, both of which are processed by a Vision Transformer). We concatenate and input these features to the Combiner, which is designed to learn a new feature representation capturing both these inputs. To address the challenge of the large volume of data in video and audio signals, another goal of the Combiner is to reduce the dimensionality of the joint video/audio inputs, which is done by selecting a smaller number of output features to be produced. The Combiner can be implemented simply as a causal Transformer, which processes the inputs in the direction of time, i.e., using only inputs of the prior steps or the current one. Alternatively, the Combiner can have a learnable memory, described below.

Combiner styles

A simple version of the Combiner adapts a Transformer architecture. More specifically, all audio and video features from the current chunk (and optionally prior chunks) are input to a Transformer and projected to a lower dimensionality, i.e., a smaller number of features are selected as the output “combined” features. While Transformers are not typically used in this context, we find it effective for reducing the dimensionality of the input features, by selecting the last m outputs of the Transformer, if m is the desired output dimension (shown below). Alternatively, the Combiner can have a memory component. For example, we use the Token Turing Machine (TTM), which supports a differentiable memory unit, accumulating and compressing features from all previous timesteps. Using a fixed memory allows the model to work with a more compact set of features at every step, rather than process all the features from previous steps, which reduces computation.

We use a simple Transformer-based Combiner (left) and a Memory Combiner (right), based on the Token Turing Machine (TTM), which uses memory to compress previous history of features.

Results

We evaluate our approach on several benchmarks, MSRVTT-QA, ActivityNet-QA and NeXT-QA, for the video QA task, where a text-based question about a video is issued and the model needs to answer. This evaluates the ability of the model to understand both the text-based question and video content, and to form an answer, focusing on only relevant information. Of these benchmarks, the latter two target long video inputs and feature more complex questions.

We also evaluate our approach in the more challenging open-ended text generation setting, wherein the model generates the answers in an unconstrained fashion as free form text, requiring an exact match to the ground truth answer. While this stricter evaluation counts synonyms as incorrect, it may better reflect a model’s ability to generalize.

Our results indicate improved performance over state-of-the-art approaches for most benchmarks, including all with open-ended generation evaluation — notable considering our model is only 3B parameters, considerably smaller than prior approaches, e.g., Flamingo 80B. We used only video and text inputs to be comparable to other work. Importantly, our model can process 512 frames without needing to increase the model parameters, which is crucial for handling longer videos. Finally with the TTM Combiner, we see both better or comparable performance while reducing compute by 18%.

Results on the MSRVTT-QA (video QA) dataset.
Results on NeXT-QA benchmark, which features long videos for the video QA task.

Results on audio-video benchmarks

Results on the popular audio-video datasets VGG-Sound and EPIC-SOUNDS are shown below. Since these benchmarks are classification-only, we treat them as an open-ended text generative setting where our model produces the text of the desired class; e.g., for the class ID corresponding to the “playing drums” activity, we expect the model to generate the text “playing drums”. In some cases our approach outperforms the prior state of the art by large margins, even though our model outputs the results in the generative open-ended setting.

Results on the VGG-Sound (audio-video QA) dataset.
Results on the EPIC-SOUNDS (audio-video QA) dataset.

Benefits of autoregressive modeling

We conduct an ablation study comparing our approach to a set of baselines that use the same input information but with standard methods (i.e., without autoregression and the Combiner). We also compare the effects of pre-training. Because standard methods are ill-suited for processing longer video, this experiment is conducted for 32 frames and four chunks only, across all settings for fair comparison. We see that Mirasol3B’s improvements are still valid for relatively short videos.

Ablation experiments comparing the main components of our model. Using the Combiner, the autoregressive modeling, and pre-training all improve performance.

Conclusion

We present a multimodal autoregressive model that addresses the challenges associated with the heterogeneity of multimodal data by coordinating the learning between time-aligned and time-unaligned modalities. Time-aligned modalities are further processed autoregressively in time with a Combiner, controlling the sequence length and producing powerful representations. We demonstrate that a relatively small model can successfully represent long video and effectively combine with other modalities. We outperform the state-of-the-art approaches (including some much bigger models) on video- and audio-video question answering.

Acknowledgements

This research is co-authored by AJ Piergiovanni, Isaac Noble, Dahun Kim, Michael Ryoo, Victor Gomes, and Anelia Angelova. We thank Claire Cui, Tania Bedrax-Weiss, Abhijit Ogale, Yunhsuan Sung, Ching-Chung Chang, Marvin Ritter, Kristina Toutanova, Ming-Wei Chang, Ashish Thapliyal, Xiyang Luo, Weicheng Kuo, Aren Jansen, Bryan Seybold, Ibrahim Alabdulmohsin, Jialin Wu, Luke Friedman, Trevor Walker, Keerthana Gopalakrishnan, Jason Baldridge, Radu Soricut, Mojtaba Seyedhosseini, Alexander D’Amour, Oliver Wang, Paul Natsev, Tom Duerig, Younghui Wu, Slav Petrov, Zoubin Ghahramani for their help and support. We also thank Tom Small for preparing the animation.

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Fine-tune and Deploy Mistral 7B with Amazon SageMaker JumpStart

Fine-tune and Deploy Mistral 7B with Amazon SageMaker JumpStart

Today, we are excited to announce the capability to fine-tune the Mistral 7B model using Amazon SageMaker JumpStart. You can now fine-tune and deploy Mistral text generation models on SageMaker JumpStart using the Amazon SageMaker Studio UI with a few clicks or using the SageMaker Python SDK.

Foundation models perform very well with generative tasks, from crafting text and summaries, answering questions, to producing images and videos. Despite the great generalization capabilities of these models, there are often use cases that have very specific domain data (such as healthcare or financial services), and these models may not be able to provide good results for these use cases. This results in a need for further fine-tuning of these generative AI models over the use case-specific and domain-specific data.

In this post, we demonstrate how to fine-tune the Mistral 7B model using SageMaker JumpStart.

What is Mistral 7B

Mistral 7B is a foundation model developed by Mistral AI, supporting English text and code generation abilities. It supports a variety of use cases, such as text summarization, classification, text completion, and code completion. To demonstrate the customizability of the model, Mistral AI has also released a Mistral 7B-Instruct model for chat use cases, fine-tuned using a variety of publicly available conversation datasets.

Mistral 7B is a transformer model and uses grouped query attention and sliding window attention to achieve faster inference (low latency) and handle longer sequences. Grouped query attention is an architecture that combines multi-query and multi-head attention to achieve output quality close to multi-head attention and comparable speed to multi-query attention. The sliding window attention method uses the multiple levels of a transformer model to focus on information that came earlier, which helps the model understand a longer stretch of context. . Mistral 7B has an 8,000-token context length, demonstrates low latency and high throughput, and has strong performance when compared to larger model alternatives, providing low memory requirements at a 7B model size. The model is made available under the permissive Apache 2.0 license, for use without restrictions.

You can fine-tune the models using either the SageMaker Studio UI or SageMaker Python SDK. We discuss both methods in this post.

Fine-tune via the SageMaker Studio UI

In SageMaker Studio, you can access the Mistral model via SageMaker JumpStart under Models, notebooks, and solutions, as shown in the following screenshot.

If you don’t see Mistral models, update your SageMaker Studio version by shutting down and restarting. For more information about version updates, refer to Shut down and Update Studio Apps.

On the model page, you can point to the Amazon Simple Storage Service (Amazon S3) bucket containing the training and validation datasets for fine-tuning. In addition, you can configure deployment configuration, hyperparameters, and security settings for fine-tuning. You can then choose Train to start the training job on a SageMaker ML instance.

Deploy the model

After the model is fine-tuned, you can deploy it using the model page on SageMaker JumpStart. The option to deploy the fine-tuned model will appear when fine-tuning is complete, as shown in the following screenshot.

Fine-tune via the SageMaker Python SDK

You can also fine-tune Mistral models using the SageMaker Python SDK. The complete notebook is available on GitHub. In this section, we provide examples of two types of fine-tuning.

Instruction fine-tuning

Instruction tuning is a technique that involves fine-tuning a language model on a collection of natural language processing (NLP) tasks using instructions. In this technique, the model is trained to perform tasks by following textual instructions instead of specific datasets for each task. The model is fine-tuned with a set of input and output examples for each task, allowing the model to generalize to new tasks that it hasn’t been explicitly trained on as long as prompts are provided for the tasks. Instruction tuning helps improve the accuracy and effectiveness of models and is helpful in situations where large datasets aren’t available for specific tasks.

Let’s walk through the fine-tuning code provided in the example notebook with the SageMaker Python SDK.

We use a subset of the Dolly dataset in an instruction tuning format, and specify the template.json file describing the input and the output formats. The training data must be formatted in JSON lines (.jsonl) format, where each line is a dictionary representing a single data sample. In this case, we name it train.jsonl.

The following snippet is an example of train.jsonl. The keys instruction, context, and response in each sample should have corresponding entries {instruction}, {context}, {response} in the template.json.

{
    "instruction": "What is a dispersive prism?", 
    "context": "In optics, a dispersive prism is an optical prism that is used to disperse light, that is, to separate light into its spectral components (the colors of the rainbow). Different wavelengths (colors) of light will be deflected by the prism at different angles. This is a result of the prism material's index of refraction varying with wavelength (dispersion). Generally, longer wavelengths (red) undergo a smaller deviation than shorter wavelengths (blue). The dispersion of white light into colors by a prism led Sir Isaac Newton to conclude that white light consisted of a mixture of different colors.", 
    "response": "A dispersive prism is an optical prism that disperses the light's different wavelengths at different angles. When white light is shined through a dispersive prism it will separate into the different colors of the rainbow."
}

The following is a sample of template.json:

{
    "prompt": "Below is an instruction that describes a task, paired with an input that provides further context. "
    "Write a response that appropriately completes the request.nn"
    "### Instruction:n{instruction}nn### Input:n{context}nn",
    "completion": " {response}",
}

After you upload the prompt template and the training data to an S3 bucket, you can set the hyperparameters.

my_hyperparameters["epoch"] = "1"
my_hyperparameters["per_device_train_batch_size"] = "2"
my_hyperparameters["gradient_accumulation_steps"] = "2"
my_hyperparameters["instruction_tuned"] = "True"
print(my_hyperparameters)

You can then start the fine-tuning process and deploy the model to an inference endpoint. In the following code, we use an ml.g5.12xlarge instance:

from sagemaker.jumpstart.estimator import JumpStartEstimator

instruction_tuned_estimator = JumpStartEstimator(
    model_id=model_id,
    hyperparameters=my_hyperparameters,
    instance_type="ml.g5.12xlarge",
)
instruction_tuned_estimator.fit({"train": train_data_location}, logs=True)

instruction_tuned_predictor = instruction_tuned_estimator.deploy()

Domain adaptation fine-tuning

Domain adaptation fine-tuning is a process that refines a pre-trained LLM to better suit a specific domain or task. By using a smaller, domain-specific dataset, the LLM can be fine-tuned to understand and generate content that is more accurate, relevant, and insightful for that specific domain, while still retaining the vast knowledge it gained during its original training.

The Mistral model can be fine-tuned on any domain-specific dataset. After it’s fine-tuned, it’s expected to generate domain-specific text and solve various NLP tasks in that specific domain. For the training dataset, provide a train directory and an optional validation directory, each containing a single CSV, JSON, or TXT file. For CSV and JSON formats, use data from the text column or the first column if text isn’t present. Ensure only one file exists under each directory. For instance, input data may be SEC filings of Amazon as a text file:

This report includes estimates, projections, statements relating to our
business plans, objectives, and expected operating results that are “forward-
looking statements” within the meaning of the Private Securities Litigation
Reform Act of 1995, Section 27A of the Securities Act of 1933, and Section 21E
of the Securities Exchange Act of 1934. Forward-looking statements may appear
throughout this report, including the following sections: “Business” (Part I,
Item 1 of this Form 10-K), “Risk Factors” (Part I, Item 1A of this Form 10-K),
and “Management’s Discussion and Analysis of Financial Condition and Results
of Operations” (Part II, Item 7 of this Form 10-K). These forward-looking
statements generally are identified by the words “believe,” “project,”
“expect,” “anticipate,” “estimate,” “intend,” “strategy,” “future,”
“opportunity,” “plan,” “may,” “should,” “will,” “would,” “will be,” “will
continue,” “will likely result,” and similar expressions.

You can start domain adaptation fine-tuning by specifying the hyperparameter “instruction_tuned” as “False“. The rest of the steps are similar to the instruction fine-tuning steps.

We set max_input_length to be 2048 on g5.12xlarge. You can use a higher input length on a larger instance type. For details, see the example notebook.

Inference example with and without fine-tuning

The following table contains the results of the Mistral 7B model fine-tuned with SEC filing documents of Amazon from 2021–2022. We compare the output before and after fine-tuning.

Input Prompt Output Before Fine-Tuning Output After Fine-Tuning Parameters
This Form 10-K report shows that

the company has been in business for 10 years and has a total of 10 employees. The company has a total of 10 employees and is in the business of providing services to the public. The company has a total of 10 employees and is in the business of providing services to the public.

The company has a total of 10 employees and is in the business of providing services to the public. The company has a total of 10 employees and is in the business of providing services to the public. The company has a total of 10 employees and is in the business of providing services to the public. The company has a total of 10 employees and is in the business of providing

 we had net sales of $1.2 billion, $1.5 billion, and $1.8 billion in 2004, 2003, and 2002. These amounts include sales from our international websites. Net sales from our international websites were $323 million, $242 million, and $150 million in 2004, 2003, and 2002. Net sales from our international websites represented 27%, 16%, and 8% of net sales in 2004, 2003, and 2002. “temperature”: 0.2,
“max_new_tokens”: 150,
We serve consumers through

our retail network of 1,700 stores, which includes 1,200 stores in the United States, 400 stores in Canada, and 100 stores in Mexico. We also serve consumers through our e-commerce websites.

## What is the biggest home improvement store?

The Home Depot is the largest home improvement retailer in the United States.

## What is the largest home improvement store in the world?

The largest home improvement store in the world is Home Depot.

## What is the biggest home improvement store in the

 our online and physical stores and focus on selection, price, and convenience. We design our stores to enable hundreds of millions of unique products to be sold by us and by third parties across dozens of product categories. Customers access our offerings through our websites, mobile apps, Alexa, devices, streaming, and physically visiting our stores. We also manufacture and sell electronic devices, including Kindle, Fire tablet, Fire TV, Echo, and Ring, and we develop and produce media content. We seek to offer our customers low prices, fast and free delivery, easy-to-use functionality, and timely customer service. In addition, we offer subscription services such as Amazon Prime, a
Our vision is

to be the leading provider of quality, affordable, and accessible health care services in the communities we serve.

Our mission is to provide quality, affordable, and accessible health care services to the communities we serve.

Our values are:

– Integrity: We act with honesty and integrity in all our dealings.
– Respect: We treat everyone with respect and dignity.
– Excellence: We strive for excellence in all we do.
– Compassion: We care for our patients and each other with compassion.
– Teamwork: We work together as a team to achieve our goals.

Our commitment to our patients is to provide quality, affordable, and accessible health care services.

 to be Earth’s most customer-centric company. We are guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. In each of our segments, we serve our primary customer sets, consisting of consumers, sellers, developers, enterprises, and content creators. In addition, we provide services, such as advertising. We have organized our operations into three segments: North America, International, and AWS. These segments reflect the way the Company evaluates its business performance and manages its operations. Information on our net sales is contained in Item 8 of Part II, “Financial Statements

As you can see, the fine-tuned model provides more specific information related to Amazon compared to the generic pre-trained one. This is because fine-tuning adapts the model to understand the nuances, patterns, and specifics of the provided dataset. By using a pre-trained model and tailoring it with fine-tuning, we ensure that you get the best of both worlds: the broad knowledge of the pre-trained model and the specialized accuracy for your unique dataset. One size may not fit all in the world of machine learning, and fine-tuning is the tailor-made solution you need!

Conclusion

In this post, we discussed fine-tuning the Mistral 7B model using SageMaker JumpStart. We showed how you can use the SageMaker JumpStart console in SageMaker Studio or the SageMaker Python SDK to fine-tune and deploy these models. As a next step, you can try fine-tuning these models on your own dataset using the code provided in the GitHub repository to test and benchmark the results for your use cases.

About the Authors

Xin HuangXin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms. He focuses on developing scalable machine learning algorithms. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering. He has published many papers in ACL, ICDM, KDD conferences, and Royal Statistical Society: Series A.

Vivek Gangasani is a AI/ML Startup Solutions Architect for Generative AI startups at AWS. He helps emerging GenAI startups build innovative solutions using AWS services and accelerated compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the inference performance of Large Language Models. In his free time, Vivek enjoys hiking, watching movies and trying different cuisines.

Dr. Ashish Khetan is a Senior Applied Scientist with Amazon SageMaker built-in algorithms and helps develop machine learning algorithms. He got his PhD from University of Illinois Urbana-Champaign. He is an active researcher in machine learning and statistical inference, and has published many papers in NeurIPS, ICML, ICLR, JMLR, ACL, and EMNLP conferences.

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Harness large language models in fake news detection

Harness large language models in fake news detection

Fake news, defined as news that conveys or incorporates false, fabricated, or deliberately misleading information, has been around as early as the emergence of the printing press. The rapid spread of fake news and disinformation online is not only deceiving to the public, but can also have a profound impact on society, politics, economy, and culture. Examples include:

  • Cultivating distrust in the media
  • Undermining the democratic process
  • Spreading false or discredited science (for example, the anti-vax movement)

Advances in artificial intelligence (AI) and machine learning (ML) have made developing tools for creating and sharing fake news even easier. Early examples include advanced social bots and automated accounts that supercharge the initial stage of spreading fake news. In general, it is not trivial for the public to determine whether such accounts are people or bots. In addition, social bots are not illegal tools, and many companies legally purchase them as part of their marketing strategy. Therefore, it’s not easy to curb the use of social bots systematically.

Recent discoveries in the field of generative AI make it possible to produce textual content at an unprecedented pace with the help of large language models (LLMs). LLMs are generative AI text models with over 1 billion parameters, and they are facilitated in the synthesis of high-quality text.

In this post, we explore how you can use LLMs to tackle the prevalent issue of detecting fake news. We suggest that LLMs are sufficiently advanced for this task, especially if improved prompt techniques such as Chain-of-Thought and ReAct are used in conjunction with tools for information retrieval.

We illustrate this by creating a LangChain application that, given a piece of news, informs the user whether the article is true or fake using natural language. The solution also uses Amazon Bedrock, a fully managed service that makes foundation models (FMs) from Amazon and third-party model providers accessible through the AWS Management Console and APIs.

LLMs and fake news

The fake news phenomenon started evolving rapidly with the advent of the internet and more specifically social media (Nielsen et al., 2017). On social media,­ fake news can be shared quickly in a user’s network, leading the public to form the wrong collective opinion. In addition, people often propagate fake news impulsively, ignoring the factuality of the content if the news resonates with their personal norms (Tsipursky et al. 2018). Research in social science has suggested that cognitive bias (confirmation bias, bandwagon effect, and choice-supportive bias) is one of the most pivotal factors in making irrational decisions in terms of the both creation and consumption of fake news (Kim, et al., 2021). This also implies that news consumers share and consume information only in the direction of strengthening their beliefs.

The power of generative AI to produce textual and rich content at an unprecedented pace aggravates the fake news problem. An example worth mentioning is deepfake technology—combining various images on an original video and generating a different video. Besides the disinformation intent that human actors bring to the mix, LLMs add a whole new set of challenges:

  • Factual errors – LLMs have an increased risk of containing factual errors due to the nature of their training and ability to be creative while generating the next words in a sentence. LLM training is based on repeatedly presenting a model with incomplete input, then using ML training techniques until it correctly fills in the gaps, thereby learning language structure and a language-based world model. Consequently, although LLMs are great pattern matchers and re-combiners (“stochastic parrots”), they fail at a number of simple tasks that require logical reasoning or mathematical deduction, and can hallucinate answers. In addition, temperature is one of the LLM input parameters that controls the behavior of the model when generating the next word in a sentence. By selecting a higher temperature, the model will use a lower-probability word, providing a more random response.
  • Lengthy – Generated texts tend to be lengthy and lack a clearly defined granularity for individual facts.
  • Lack of fact-checking – There is no standardized tooling available for fact-checking during the process of text generation.

Overall, the combination of human psychology and limitations of AI systems has created a perfect storm for the proliferation of fake news and misinformation online.

Solution overview

LLMs are demonstrating outstanding capabilities in language generation, understanding, and few-shot learning. They are trained on a vast corpus of text from the internet, where quality and accuracy of extracted natural language may not be assured.

In this post, we provide a solution to detect fake news based both on Chain-of-Thought and Re-Act (Reasoning and Acting) prompt approaches. First, we discuss those two prompt engineering techniques, then we show their implementation using LangChain and Amazon Bedrock.

The following architecture diagram outlines the solution for our fake news detector.

Architecture diagram for fake news detection.

We use a subset of the FEVER dataset containing a statement and the ground truth about the statement indicating false, true, or unverifiable claims (Thorne J. et al., 2018).

The workflow can be broken down into the following steps:

  1. The user selects one of the statements to check if fake or true.
  2. The statement and the fake news detection task are incorporated into the prompt.
  3. The prompt is passed to LangChain, which invokes the FM in Amazon Bedrock.
  4. Amazon Bedrock returns a response to the user request with the statement True or False.

In this post, we use the Claude v2 model from Anthrophic (anthropic.claude-v2). Claude is a generative LLM based on Anthropic’s research into creating reliable, interpretable, and steerable AI systems. Created using techniques like constitutional AI and harmlessness training, Claude excels at thoughtful dialogue, content creation, complex reasoning, creativity, and coding. However, by using Amazon Bedrock and our solution architecture, we also have the flexibility to choose among other FMs provided by Amazon, AI21labs, Cohere, and Stability.ai.

You can find the implementation details in the following sections. The source code is available in the GitHub repository.

Prerequisites

For this tutorial, you need a bash terminal with Python 3.9 or higher installed on either Linux, Mac, or a Windows Subsystem for Linux and an AWS account.

We also recommend using either an Amazon SageMaker Studio notebook, an AWS Cloud9 instance, or an Amazon Elastic Compute Cloud (Amazon EC2) instance.

Deploy fake news detection using the Amazon Bedrock API

The solution uses the Amazon Bedrock API, which can be accessed using the AWS Command Line Interface (AWS CLI), the AWS SDK for Python (Boto3), or an Amazon SageMaker notebook. Refer to the Amazon Bedrock User Guide for more information. For this post, we use the Amazon Bedrock API via the AWS SDK for Python.

Set up Amazon Bedrock API environment

To set up your Amazon Bedrock API environment, complete the following steps:

  1. Download the latest Boto3 or upgrade it: 
    pip install --upgrade boto3

  2. Make sure you configure the AWS credentials using the aws configure command or pass them to the Boto3 client.
  3. Install the latest version of LangChain: 
    pip install “langchain>=0.0.317” --quiet

You can now test your setup using the following Python shell script. The script instantiates the Amazon Bedrock client using Boto3. Next, we call the list_foundation_models API to get the list of foundation models available for use.

import boto3 
import json 
bedrock = boto3.client( 'bedrock', region_name=YOUR_REGION) 
print(json.dumps(bedrock.list_foundation_models(), indent=4))

After successfully running the preceding command, you should get the list of FMs from Amazon Bedrock.

LangChain as a prompt chaining solution

To detect fake news for a given sentence, we follow the zero-shot Chain-of-Thought reasoning process (Wei J. et al., 2022), which is composed of the following steps:

  1. Initially, the model attempts to create a statement about the news prompted.
  2. The model creates a bullet point list of assertions.
  3. For each assertion, the model determines if the assertion is true or false. Note that using this methodology, the model relies exclusively on its internal knowledge (weights computed in the pre-training phase) to reach a verdict. The information is not verified against any external data at this point.
  4. Given the facts, the model answers TRUE or FALSE for the given statement in the prompt.

To achieve these steps, we use LangChain, a framework for developing applications powered by language models. This framework allows us to augment the FMs by chaining together various components to create advanced use cases. In this solution, we use the built-in SimpleSequentialChain in LangChain to create a simple sequential chain. This is very useful, because we can take the output from one chain and use it as the input to another.

Amazon Bedrock is integrated with LangChain, so you only need to instantiate it by passing the model_id when instantiating the Amazon Bedrock object. If needed, the model inference parameters can be provided through the model_kwargs argument, such as:

  • maxTokenCount – The maximum number of tokens in the generated response
  • stopSequences – The stop sequence used by the model
  • temperature – A value that ranges between 0–1, with 0 being the most deterministic and 1 being the most creative
  • top – A value that ranges between 0–1, and is used to control tokens’ choices based on the probability of the potential choices

If this is the first time you are using an Amazon Bedrock foundational model, make sure you request access to the model by selecting from the list of models on the Model access page on the Amazon Bedrock console, which in our case is claude-v2 from Anthropic.

from langchain.llms.bedrock import Bedrock
bedrock_runtime = boto3.client(
    service_name='bedrock-runtime',
    region_name= YOUR_REGION,
)
model_kwargs={
        'max_tokens_to_sample': 8192
    }
llm = Bedrock(model_id=" anthropic.claude-v2", client=bedrock_runtime, model_kwargs=model_kwargs)

The following function defines the Chain-of-Thought prompt chain we mentioned earlier for detecting fake news. The function takes the Amazon Bedrock object (llm) and the user prompt (q) as arguments. LangChain’s PromptTemplate functionality is used here to predefine a recipe for generating prompts.

from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.chains import SimpleSequentialChain

def generate_and_print(llm, q):
    total_prompt = """"""

    # the model is asked to create a bullet point list of assertions
    template = """Here is a statement:
    {statement}
    Make a bullet point list of the assumptions you made when given the above statement.nn"""
    prompt_template = PromptTemplate(input_variables=["statement"], template=template)
    assumptions_chain = LLMChain(llm=llm, prompt=prompt_template)
    total_prompt = total_prompt + template

    # the model is asked to create a bullet point list of assertions    
    template = """Here is a bullet point list of assertions:
    {assertions}
    For each assertion, determine whether it is true or false. If it is false, explain why.nn"""
    prompt_template = PromptTemplate(input_variables=["assertions"], template=template)
    fact_checker_chain = LLMChain(llm=llm, prompt=prompt_template)
    total_prompt = total_prompt + template

    #for each assertion, the model is askded to determine if the assertion is true or false, based on internal knowledge alone

    template = """ Based on the above assertions, the final response is FALSE if one of the assertions is FALSE. Otherwise, the final response is TRUE. You should only respond with TRUE or FALSE.'{}'""".format(q)
    template = """{facts}n""" + template
    prompt_template = PromptTemplate(input_variables=["facts"], template=template)
    answer_chain = LLMChain(llm=llm, prompt=prompt_template)
    total_prompt = total_prompt + template

    #SimpleSequentialChain allows us to take the output from one chain and use it as the input to another
    overall_chain = SimpleSequentialChain(chains=[assumptions_chain, fact_checker_chain, answer_chain], verbose=True)
    answer = overall_chain.run(q)

    return answer

The following code calls the function we defined earlier and provides the answer. The statement is TRUE or FALSE. TRUE means that the statement provided contains correct facts, and FALSE means that the statement contains at least one incorrect fact.

from IPython.display import display, Markdown

q="The first woman to receive a Ph.D. in computer science was Dr. Barbara Liskov, who earned her degree from Stanford University in 1968."
print(f'The statement is: {q}')
display(Markdown(generate_and_print(llm, q)))

An example of a statement and model response is provided in the following output:

The statement is: The first woman to receive a Ph.D. in computer science was Dr. Barbara Liskov, who earned her degree from Stanford University in 1968.

> Entering new SimpleSequentialChain chain...
 Here is a bullet point list of assumptions I made about the statement:

- Dr. Barbara Liskov was the first woman to earn a Ph.D. in computer science. 

- Dr. Liskov earned her Ph.D. from Stanford University.

- She earned her Ph.D. in 1968.

- No other woman earned a Ph.D. in computer science prior to 1968.

- Stanford University had a computer science Ph.D. program in 1968. 

- The statement refers to Ph.D. degrees earned in the United States.
 Here are my assessments of each assertion:

- Dr. Barbara Liskov was the first woman to earn a Ph.D. in computer science.
  - True. Dr. Liskov was the first American woman to earn a Ph.D. in computer science, which she received from Stanford University in 1968.

- Dr. Liskov earned her Ph.D. from Stanford University.
  - True. Multiple sources confirm she received her Ph.D. from Stanford in 1968.

- She earned her Ph.D. in 1968.
  - True. This is consistent across sources.

- No other woman earned a Ph.D. in computer science prior to 1968.
  - False. While she was the first American woman, Mary Kenneth Keller earned a Ph.D. in computer science from the University of Wisconsin in 1965. However, Keller earned her degree in the US as well.

- Stanford University had a computer science Ph.D. program in 1968.
  - True. Stanford established its computer science department and Ph.D. program in 1965.

- The statement refers to Ph.D. degrees earned in the United States.
  - False. The original statement does not specify the country. My assumptions that it refers to the United States is incorrect. Keller earned her Ph.D. in the US before Liskov.
 False

ReAct and tools

In the preceding example, the model correctly identified that the statement is false. However, submitting the query again demonstrates the model’s inability to distinguish the correctness of facts. The model doesn’t have the tools to verify the truthfulness of statements beyond its own training memory, so subsequent runs of the same prompt can lead it to mislabel fake statements as true. In the following code, you have a different run of the same example:

The statement is: The first woman to receive a Ph.D. in computer science was Dr. Barbara Liskov, who earned her degree from Stanford University in 1968.

> Entering new SimpleSequentialChain chain...
 Here is a bullet point list of assumptions I made about the statement:

- Dr. Barbara Liskov was the first woman to earn a Ph.D. in computer science
- Dr. Liskov earned her Ph.D. degree in 1968 
- Dr. Liskov earned her Ph.D. from Stanford University
- Stanford University awarded Ph.D. degrees in computer science in 1968
- Dr. Liskov was a woman
- Ph.D. degrees existed in 1968
- Computer science existed as a field of study in 1968
 Here are my assessments of each assertion:

- Dr. Barbara Liskov was the first woman to earn a Ph.D. in computer science
    - True. Dr. Liskov was the first woman to earn a Ph.D. in computer science in 1968 from Stanford University.

- Dr. Liskov earned her Ph.D. degree in 1968
    - True. Multiple sources confirm she received her Ph.D. in computer science from Stanford in 1968.

- Dr. Liskov earned her Ph.D. from Stanford University 
    - True. Dr. Liskov earned her Ph.D. in computer science from Stanford University in 1968.

- Stanford University awarded Ph.D. degrees in computer science in 1968
    - True. Stanford awarded Liskov a Ph.D. in computer science in 1968, so they offered the degree at that time.

- Dr. Liskov was a woman
    - True. All biographical information indicates Dr. Liskov is female.

- Ph.D. degrees existed in 1968
    - True. Ph.D. degrees have existed since the late 19th century.

- Computer science existed as a field of study in 1968
    - True. While computer science was a relatively new field in the 1960s, Stanford and other universities offered it as a field of study and research by 1968.
 True

One technique for guaranteeing truthfulness is ReAct. ReAct (Yao S. et al., 2023) is a prompt technique that augments the foundation model with an agent’s action space. In this post, as well as in the ReAct paper, the action space implements information retrieval using search, lookup, and finish actions from a simple Wikipedia web API.

The reason behind using ReAct in comparison to Chain-of-Thought is to use external knowledge retrieval to augment the foundation model to detect if a given piece of news is fake or true.

In this post, we use LangChain’s implementation of ReAct through the agent ZERO_SHOT_REACT_DESCRIPTION. We modify the previous function to implement ReAct and use Wikipedia by using the load_tools function from the langchain.agents.

We also need to install the Wikipedia package:

!pip install Wikipedia

Below is the new code:

from langchain.agents import load_tools, initialize_agent, AgentType

def generate_and_print(llm, q):
    print(f'Inside generate_and_print: q = {q}')
    tools = load_tools(["wikipedia"], llm=llm)
    agent = initialize_agent(tools, llm, 
                             agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, 
                             verbose=True,
                             handle_parsing_errors=True,
                             agent_kwargs={})

    input = """Here is a statement:
    {statement}
    Is this statement correct? You can use tools to find information if needed.
    The final response is FALSE if the statement is FALSE. Otherwise, TRUE."""

    answer = agent.run(input.format(statement=q))
    
    return answer

The following is the output of the preceding function given the same statement used before:

> Entering new AgentExecutor chain...
 Here are my thoughts and actions to determine if the statement is true or false:

Thought: To verify if this statement about the first woman to receive a PhD in computer science is true, I should consult a reliable information source like Wikipedia.

Action: Wikipedia
Action Input: first woman to receive phd in computer science
Observation: Page: Fu Foundation School of Engineering and Applied Science
Summary: The Fu Foundation School of Engineering and Applied Science (popularly known as SEAS or Columbia Engineering; previously known as Columbia School of Mines) is the engineering and applied science school of Columbia University. It was founded as the School of Mines in 1863 and then the School of Mines, Engineering and Chemistry before becoming the School of Engineering and Applied Science. On October 1, 1997, the school was renamed in honor of Chinese businessman Z.Y. Fu, who had donated $26 million to the school.
The Fu Foundation School of Engineering and Applied Science maintains a close research tie with other institutions including NASA, IBM, MIT, and The Earth Institute. Patents owned by the school generate over $100 million annually for the university. SEAS faculty and alumni are responsible for technological achievements including the developments of FM radio and the maser.
The School's applied mathematics, biomedical engineering, computer science and the financial engineering program in operations research are very famous and highly ranked. The current SEAS faculty include 27 members of the National Academy of Engineering and one Nobel laureate. In all, the faculty and alumni of Columbia Engineering have won 10 Nobel Prizes in physics, chemistry, medicine, and economics.
The school consists of approximately 300 undergraduates in each graduating class and maintains close links with its undergraduate liberal arts sister school Columbia College which shares housing with SEAS students. The School's current dean is Shih-Fu Chang, who was appointed in 2022.

Page: Doctor of Science
Summary: A Doctor of Science (Latin: Scientiae Doctor; most commonly abbreviated DSc or ScD) is an academic research doctorate awarded in a number of countries throughout the world. In some countries, a Doctor of Science is the degree used for the standard doctorate in the sciences; elsewhere a Doctor of Science is a "higher doctorate" awarded in recognition of a substantial and sustained contribution to scientific knowledge beyond that required for a Doctor of Philosophy (PhD).

Page: Timeline of women in science
Summary: This is a timeline of women in science, spanning from ancient history up to the 21st century. While the timeline primarily focuses on women involved with natural sciences such as astronomy, biology, chemistry and physics, it also includes women from the social sciences (e.g. sociology, psychology) and the formal sciences (e.g. mathematics, computer science), as well as notable science educators and medical scientists. The chronological events listed in the timeline relate to both scientific achievements and gender equality within the sciences.
Thought: Based on the Wikipedia pages, the statement appears to be false. The Wikipedia Timeline of Women in Science page indicates that Adele Goldstine was the first woman to earn a PhD in computer science in 1964 from the University of Michigan, not Barbara Liskov from Stanford in 1968. Therefore, my final answer is:

Final Answer: FALSE

Clean up

To save costs, delete all the resources you deployed as part of the tutorial. If you launched AWS Cloud9 or an EC2 instance, you can delete it via the console or using the AWS CLI. Similarly, you can delete the SageMaker notebook you may have created via the SageMaker console.

Limitations and related work

The field of fake news detection is actively researched in the scientific community. In this post, we used Chain-of-Thought and ReAct techniques and in evaluating the techniques, we only focused on the accuracy of the prompt technique classification (if a given statement is true or false). Therefore, we haven’t considered other important aspects such as speed of the response, nor extended the solution to additional knowledge base sources besides Wikipedia.

Although this post focused on two techniques, Chain-of-Thought and ReAct, an extensive body of work has explored how LLMs can detect, eliminate or mitigate fake news. Lee et al. has proposed the use of an encoder-decoder model using NER (named entity recognition) to mask the named entities in order to ensure that the token masked actually uses the knowledge encoded in the language model. Chern et.al. developed FacTool, which uses Chain-of-Thought principles to extract claims from the prompt, and consequently collect relevant evidences of the claims. The LLM then judges the factuality of the claim given the retrieved list of evidences. Du E. et al. presents a complementary approach where multiple LLMs propose and debate their individual responses and reasoning processes over multiple rounds in order to arrive at a common final answer.

Based on the literature, we see that the effectiveness of LLMs in detecting fake news increases when the LLMs are augmented with external knowledge and multi-agent conversation capability. However, these approaches are more computationally complex because they require multiple model calls and interactions, longer prompts, and lengthy network layer calls. Ultimately, this complexity translates into an increased overall cost. We recommend assessing the cost-to-performance ratio before deploying similar solutions in production.

Conclusion

In this post, we delved into how to use LLMs to tackle the prevalent issue of fake news, which is one of the major challenges of our society nowadays. We started by outlining the challenges presented by fake news, with an emphasis on its potential to sway public sentiment and cause societal disruptions.

We then introduced the concept of LLMs as advanced AI models that are trained on a substantial quantity of data. Due to this extensive training, these models boast an impressive understanding of language, enabling them to produce human-like text. With this capacity, we demonstrated how LLMs can be harnessed in the battle against fake news by using two different prompt techniques, Chain-of-Thought and ReAct.

We underlined how LLMs can facilitate fact-checking services on an unparalleled scale, given their capability to process and analyze vast amounts of text swiftly. This potential for real-time analysis can lead to early detection and containment of fake news. We illustrated this by creating a Python script that, given a statement, highlights to the user whether the article is true or fake using natural language.

We concluded by underlining the limitations of the current approach and ended on a hopeful note, stressing that, with the correct safeguards and continuous enhancements, LLMs could become indispensable tools in the fight against fake news.

We’d love to hear from you. Let us know what you think in the comments section, or use the issues forum in the GitHub repository.

Disclaimer: The code provided in this post is meant for educational and experimentation purposes only. It should not be relied upon to detect fake news or misinformation in real-world production systems. No guarantees are made about the accuracy or completeness of fake news detection using this code. Users should exercise caution and perform due diligence before utilizing these techniques in sensitive applications.

To get started with Amazon Bedrock, visit the Amazon Bedrock console.

About the authors

Anamaria Todor is a Principal Solutions Architect based in Copenhagen, Denmark. She saw her first computer when she was 4 years old and never let go of computer science, video games, and engineering since. She has worked in various technical roles, from freelancer, full-stack developer, to data engineer, technical lead, and CTO, at various companies in Denmark, focusing on the gaming and advertising industries. She has been at AWS for over 3 years, working as a Principal Solutions Architect, focusing mainly on life sciences and AI/ML. Anamaria has a bachelor’s in Applied Engineering and Computer Science, a master’s degree in Computer Science, and over 10 years of AWS experience. When she’s not working or playing video games, she’s coaching girls and female professionals in understanding and finding their path through technology.

Marcel Castro is a Senior Solutions Architect based in Oslo, Norway. In his role, Marcel helps customers with architecture, design, and development of cloud-optimized infrastructure. He is a member of the AWS Generative AI Ambassador team with the goal to drive and support EMEA customers on their generative AI journey. He holds a PhD in Computer Science from Sweden and a master’s and bachelor’s degree in Electrical Engineering and Telecommunications from Brazil.

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