open-compass/ANAH
[ACL 2024] ANAH & [NeurIPS 2024] ANAH-v2 & [ICLR 2025] Mask-DPO
This project helps anyone evaluating or building large language models (LLMs) to identify and reduce 'hallucinations' — instances where LLMs generate factually incorrect information. It takes LLM-generated text as input and provides detailed annotations about hallucinated sentences, along with a factuality score. The output can be used to improve the reliability of LLMs or to compare the performance of different models.
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Use this if you need to precisely measure the factual accuracy of LLM responses, fine-tune an LLM to reduce its tendency to hallucinate, or benchmark different LLMs for factual correctness.
Not ideal if you are looking for a simple, out-of-the-box solution for general text generation without a focus on factuality or advanced LLM development.
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Language
Python
License
Apache-2.0
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Last pushed
Apr 30, 2025
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