Summary of Cheating Automatic Llm Benchmarks: Null Models Achieve High Win Rates, by Xiaosen Zheng et al.
Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates
by Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Jing Jiang, Min Lin
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 Medium Difficulty Summary: This paper examines the vulnerability of automatic benchmarks for language models to manipulation and cheating. Researchers have developed mechanisms to control model output length and style, but these may not be effective against sophisticated cheating tactics. The authors demonstrate that even a “null model” can achieve top-ranked win rates on popular benchmarks like AlpacaEval 2.0, Arena-Hard-Auto, and MT-Bench by manipulating the model’s responses. This highlights the need for anti-cheating mechanisms to ensure reliable evaluation of language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Researchers are testing how well computer programs can understand human language. They found that even a very simple program can get high scores on these tests if it cheats and gives answers that aren’t really based on what the person is saying. This means that these tests might not be giving us an accurate picture of how well the programs are doing. The authors think we need to come up with new ways to test these programs so we don’t get tricked into thinking they’re better than they actually are. |
