Top 4 Python factuality Projects
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OpenFactVerification
Loki: Open-source solution designed to automate the process of verifying factuality
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long-form-factuality
Benchmarking long-form factuality in large language models. Original code for our paper "Long-form factuality in large language models".
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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FActScore
A package to evaluate factuality of long-form generation. Original implementation of our EMNLP 2023 paper "FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation"
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RefChecker
RefChecker provides automatic checking pipeline and benchmark dataset for detecting fine-grained hallucinations generated by Large Language Models.
Project mention: Show HN: Loki Needs You – Collaborate on an Open-Source Fact-Checking AI | news.ycombinator.com | 2024-04-16
Project mention: An Open Source Tool for Multimodal Fact Verification | news.ycombinator.com | 2024-04-06Isn'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...
Looks like a slight modification of FActScore [1], but instead of using Wikipedia as a verification source, they use Google Search. They also claim to include a wider range of topics. That said, FActScore allows you to use whatever knowledge source and topics you want [2].
[1]: https://arxiv.org/abs/2305.14251
[2]: https://github.com/shmsw25/FActScore?tab=readme-ov-file#to-u...
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)
Python factuality related posts
Index
What are some of the best open-source factuality projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | OpenFactVerification | 883 |
| 2 | long-form-factuality | 459 |
| 3 | FActScore | 217 |
| 4 | RefChecker | 198 |
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