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HalluChecker is a visual QA tool designed to help users assess and compare the performance of large language models (LLMs) by evaluating their tendency to hallucinate. It provides a leaderboard-style interface to display the results of hallucination checks, making it easier to understand and benchmark different models.
• Leaderboard Display: Visualizes the performance of various LLMs based on hallucination checks.
• Hallucination Tracking: Monitors and records instances where models generate inaccurate or nonsensical information.
• Model Benchmarking: Allows users to compare the reliability of different LLMs side by side.
• Multi-Model Support: Compatible with a wide range of LLM providers and models.
• Real-Time Updates: Provides up-to-the-minute data on model performance.
• Custom Analysis: Offers filters and sorting options to refine the leaderboard based on specific criteria.
What is HalluChecker used for?
HalluChecker is used to evaluate and compare the accuracy of large language models by identifying instances of hallucination, where the model generates false or nonsensical information.
How do I interpret the leaderboard?
The leaderboard ranks LLMs based on their performance in hallucination checks. Lower scores indicate better performance, as they reflect fewer instances of hallucination.
Can HalluChecker support custom models?
Yes, HalluChecker is designed to be flexible and can support custom models. Contact the development team for specific integration requirements.