AI BriefingHugging Face Releases Guide for Building Leaderboards
Key point
It introduces the technical method of building a custom LLM leaderboard using Hugging Face's new template, along with Vectara's case study.
Details
Hugging Face has released Leaderboard Templates, which are lighter and easier to use than the existing Open LLM Leaderboard.
Vectara shared how it used this template to build its own Hughes Hallucination Evaluation Model (HHEM) leaderboard. HHEM specializes in measuring the frequency of hallucinations occurring in summaries generated by various models such as GPT-4, Gemini, and Llama 2.
The main technical steps for building a leaderboard are as follows:
- Dataset Management: Create a
requestsdataset to log user requests and aresultsdataset to store evaluation results, enabling dynamic updates. - Backend Customization: Modify
model_operations.pyto implement complex evaluation logic, integrating features that generate summaries and calculate Factual Consistency, Hallucination Rate, and more.
This guide provides developers with a practical, open-source-based methodology for building and managing their own model evaluation frameworks.
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