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Source Attribution Module

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Source Attribution Module

The Source Attribution Module enhances the transparency and trustworthiness of Large Language Model (LLM) outputs by providing clear and verifiable source attributions for the information they generate. While LLMs are trained on vast amounts of data, they don't inherently track or cite the origin of specific facts or statements. This module addresses this critical issue by attempting to identify and attribute the sources of information used by the LLM.

The module works by:

  • Information Extraction: The module analyzes the LLM's output and identifies specific factual claims or statements.
  • Source Matching: It compares these claims against a database of known sources (e.g., web pages, articles, books, databases) using techniques like semantic similarity and knowledge graph matching.
  • Citation Generation: When a matching source is found, the module generates a citation or link to the source, providing users with verifiable evidence for the information provided by the LLM.
  • Confidence Scoring for Attributions: The module assigns a confidence score to each attribution based on the strength of the match between the LLM's output and the identified source.
  • Handling of Unattributable Information: If a claim cannot be confidently attributed to a specific source, the module provides a clear indication that the information is not verifiable, preventing users from mistaking generated content for established fact.

This patch is invaluable for applications requiring high levels of accuracy, transparency, and accountability, such as journalism, research, education, and legal analysis. It integrates seamlessly with prominent LLMs.

Use Cases/Instances Where It's Needed:

  • Journalistic Content Generation: Providing verifiable sources for news articles and reports generated by LLMs.
  • Academic Research and Writing: Ensuring proper citation of sources in research papers and academic publications.
  • Educational Platforms and Learning Tools: Providing students with verifiable information and preventing the spread of misinformation.
  • Legal Research and Document Analysis: Ensuring the accuracy and traceability of legal information.
  • Fact-Checking and Verification Tools: Enhancing fact-checking tools by automating the process of source attribution.

Value Proposition:

  • Increased Transparency and Trust: Provides users with clear and verifiable sources for the information generated by LLMs.
  • Improved Accuracy and Reliability: Reduces the risk of misinformation and enhances the trustworthiness of LLM outputs.
  • Supports Responsible AI Practices: Promotes transparency and accountability in the use of LLMs.
  • Facilitates Fact-Checking and Verification: Makes it easier for users to verify the information provided by LLMs.
  • Seamless Integration: Designed for easy integration with existing LLM workflows.
License Option
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Published:

Oct 17, 2024 22:58 PM

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Files Included:

Patch , Licence , Documentation

Foundational Models:

GPT-4 , GPT-3.5 , Claude , Llama 2 , PaLM 2 , Cohere , Jurassic-2 , StableLM , Bard , LaMDA , BERT , Transformer-XL , XLNet , RoBERTa , Albert , DistilBERT , Electra , T5 , Megatron-Turing NLG , BLOOM , OPT , Gopher , Chinchilla
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