Summary of Antileak-bench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-world Knowledge, By Xiaobao Wu et al.
AntiLeak-Bench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge
by Xiaobao Wu, Liangming Pan, Yuxi Xie, Ruiwen Zhou, Shuai Zhao, Yubo Ma, Mingzhe Du, Rui Mao, Anh Tuan Luu, William Yang Wang
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper addresses a crucial issue in evaluating large language models (LLMs): data contamination. Existing solutions update benchmarks with new data but may not guarantee fair evaluation, as the new data could contain pre-existing knowledge. To overcome these limitations, the authors propose AntiLeak-Bench, an automated framework that constructs samples with explicitly new knowledge absent from LLMs’ training sets. This ensures strictly contamination-free evaluation. The proposed workflow also eliminates the need for human labor, reducing the cost of benchmark maintenance. Experimental results demonstrate that data contamination is likely to occur before an LLM’s cutoff time and show that AntiLeak-Bench effectively addresses this challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make sure that large language models are tested fairly by preventing old information from getting into their training sets. Right now, people solve this problem by collecting new data for testing, but this can still introduce old knowledge. The authors came up with a way to fix this by creating test samples with completely new information that the model hasn’t seen before. They also made it easy to update these tests without needing human help, making it cheaper and faster. |
