RefChecker
RefChecker provides automatic checking pipeline and benchmark dataset for detecting fine-grained hallucinations generated by Large Language Models. (by amazon-science)
long-form-factuality
Benchmarking long-form factuality in large language models. Original code for our paper "Long-form factuality in large language models". (by google-deepmind)
RefChecker | long-form-factuality | |
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1 | 2 | |
213 | 477 | |
2.8% | 8.8% | |
7.6 | 6.1 | |
11 days ago | 26 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
RefChecker
Posts with mentions or reviews of RefChecker.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-05-03.
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How to Detect AI Hallucinations
RefChecker operates through a 3-stage pipeline: 1. Triplets Extraction: Utilizes LLMs to break down text into knowledge triplets for detailed analysis. 2. Checker Stage: Predicts hallucination labels on the extracted triplets using LLM-based or NLI-based checkers. 3. Aggregation: Combines individual triplet-level results to determine the overall hallucination label for the input text based on predefined rules. Additionally, RefChecker includes a human labeling tool, a search engine for Zero Context settings, and a localization model to map knowledge triplets back to reference snippets for comprehensive analysis. Triplets in the context of RefChecker refer to knowledge units extracted from text using Large Language Models (LLMs). These triplets consist of three elements that capture essential information from the text. The extraction of triplets helps in finer-grained detection and evaluation of claims by breaking down the original text into structured components for analysis. The triplets play a crucial role in detecting hallucinations and assessing the factual accuracy of claims made by language models. RefChecker includes support for various Large Language Models (LLMs) that can be used locally for processing and analysis. Some of the popular LLMs supported by RefChecker include GPT4, GPT-3.5-Turbo, InstructGPT, Falcon, Alpaca, LLaMA2, and Claude 2. These models can be utilized within the RefChecker framework for tasks such as response generation, claim extraction, and hallucination detection without the need for external connections to cloud-based services. I did not use it as it requires integration with several other providers or a large GPU for Mistral model. But this looks very promising and In future I will come back to this one (depends on how much I want to spend on GPU for my open source project)
long-form-factuality
Posts with mentions or reviews of long-form-factuality.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-06.
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An Open Source Tool for Multimodal Fact Verification
Isn't this similar to the Deepmind paper on long form factuality posted a few days ago?
https://arxiv.org/abs/2403.18802
https://github.com/google-deepmind/long-form-factuality/tree...
- LongFact – Long-Form Factuality in Large Language Models