Summary of Treeeval: Benchmark-free Evaluation Of Large Language Models Through Tree Planning, by Xiang Li et al.
TreeEval: Benchmark-Free Evaluation of Large Language Models through Tree Planning
by Xiang Li, Yunshi Lan, Chao Yang
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel benchmark-free evaluation method for large language models (LLMs) called TreeEval. The approach uses a high-performance LLM as an examiner, which hosts an irreproachable evaluation session and avoids data leakage issues. The method employs a tree planning strategy to decide the next question generation based on the current evaluation status, ensuring completeness and efficiency. Six LLMs of different sizes are evaluated, including 7B, 13B, and 33B, with TreeEval achieving the highest correlation coefficient with AlpacaEval2.0 using only around 45 questions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TreeEval is a new way to test how well large language models do their job. It lets a really good model host an evaluation session without sharing its answers or questions, so it’s fair and safe. The model uses a special plan to decide what questions to ask next based on how the evaluation is going, making sure everything gets checked and doesn’t take too long. |
