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Search for model performance across languages and benchmarks
Display benchmark results
Browse and submit model evaluations in LLM benchmarks
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Measure over-refusal in LLMs using OR-Bench
The LLM HALLUCINATIONS TOOL is a specialized application designed for evaluating the accuracy of outputs generated by large language models (LLMs). It helps users identify hallucinations, which are instances where an AI generates content that is not based on actual data or context. This tool is particularly useful for developers, researchers, and users who need to benchmark and improve the performance of LLMs.
• Automated Benchmarking: Evaluate LLM outputs against ground truth data to detect hallucinations.
• Hallucination Detection: Identify and flag AI-generated text that contains inaccuracies or fabricated information.
• Multi-Model Support: Compare performance across different LLMs to determine which models produce more accurate results.
• Accuracy Analytics: Generate detailed reports highlighting areas where the model struggles with factual accuracy.
• Customizable Evaluation: Define specific criteria for testing, such as domain-specific knowledge or factual accuracy.
• Results Visualization: Present findings in a user-friendly format, including charts and graphs, to simplify analysis.
What is a hallucination in the context of LLMs?
A hallucination occurs when an LLM generates content that is not based on any provided data or context, often leading to factual errors or nonsensical responses.
Which LLM models are supported by the tool?
The tool supports a wide range of LLMs, including popular models like GPT, PaLM, and others. For a full list, refer to the official documentation.
How do I access the LLM Hallucinations Tool?
The tool can be accessed through its official website or repository. Refer to the installation guide for step-by-step instructions.