🔢Hallucination detector for Large Language Models.
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Updated
Mar 5, 2024
🔢Hallucination detector for Large Language Models.
Antibodies for LLMs hallucinations (grouping LLM as a judge, NLI, reward models)
[ACL 2024] ANAH: Analytical Annotation of Hallucinations in Large Language Models
X5 Tech AI Hackathon 2024 - Hallucination Detection
Hallucination in Chat-bots: Faithful Benchmark for Information-Seeking Dialogue
up-to-date and curated list of awesome state-of-the-art LVLMs hallucinations research work, papers & resources
VideoHallucer, The first comprehensive benchmark for hallucination detection in large video-language models (LVLMs)
Competition: SemEval-2024 Task-6 - SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes
[ACL 2024] An Easy-to-use Hallucination Detection Framework for LLMs.
An Easy-to-use Hallucination Detection Framework for LLMs.
AIMon Rely is a state-of-the-art system consisting of multiple models for detecting LLM quality issues during offline evaluations and continuous production monitoring. We offer various model quality metrics that are fast, reliable and cost-effective.
Official repo for the paper PHUDGE: Phi-3 as Scalable Judge. Evaluate your LLMs with or without custom rubric, reference answer, absolute, relative and much more. It contains a list of all the available tool, methods, repo, code etc to detect hallucination, LLM evaluation, grading and much more.
[ACL 2024] Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation
UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform root cause analysis on failure cases and give insights on how to resolve them.
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