ictnlp/TruthX
Code for ACL 2024 paper "TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space"
This helps users get more accurate and truthful responses from Large Language Models (LLMs) like Llama-2. It takes an existing LLM and enhances its ability to provide factual information, reducing 'hallucinations' or made-up details. The output is a more reliable LLM that can be directly used for various tasks, benefiting anyone who uses LLMs for information retrieval or content generation, such as researchers, content creators, or customer support teams.
143 stars. No commits in the last 6 months.
Use this if you need to significantly improve the factual accuracy and reduce errors in information generated by a Large Language Model.
Not ideal if your primary concern is generating highly creative, imaginative, or fictional content where factual accuracy is secondary.
Stars
143
Forks
7
Language
Python
License
GPL-3.0
Category
Last pushed
Mar 26, 2024
Commits (30d)
0
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