zjunlp/FactCHD
[IJCAI 2024] FactCHD: Benchmarking Fact-Conflicting Hallucination Detection
This project helps anyone working with Large Language Models (LLMs) to identify and address 'hallucinations' where the model generates factually incorrect information. It takes in pairs of questions and LLM-generated answers, along with supporting evidence, and determines whether the answer is factual or not, providing a justification. This is for professionals like content reviewers, AI ethicists, or quality assurance specialists who need to ensure the accuracy of LLM outputs across various domains.
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Use this if you need a reliable way to benchmark and detect when your LLM is generating information that contradicts established facts, especially in complex scenarios like multi-step reasoning.
Not ideal if you are looking for a tool to fix the underlying issues in your LLM that cause hallucinations, as this project focuses solely on detection and evaluation.
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Language
Python
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MIT
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Last pushed
Apr 28, 2024
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