RefChecker Alternatives
Similar projects and alternatives to RefChecker based on common topics and language
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hallucination-leaderboard
Leaderboard Comparing LLM Performance at Producing Hallucinations when Summarizing Short Documents
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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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selfcheckgpt
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
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AI
Explore the forefront of AI innovation with this dedicated repository, housing cutting-edge examples and implementations. Dive into the latest advancements, stay ahead with groundbreaking applications, and harness the power of state-of-the-art models and techniques. Elevate your understanding of artificial intelligence through hands-on work (by vishalmysore)
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OpenFactVerification
Loki: Open-source solution designed to automate the process of verifying factuality
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Woodpecker
✨✨Woodpecker: Hallucination Correction for Multimodal Large Language Models. The first work to correct hallucinations in MLLMs. (by BradyFU)
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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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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
RefChecker reviews and mentions
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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)
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amazon-science/RefChecker is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of RefChecker is Python.
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