Papers by Liyao Li
Data Contamination Calibration for Black-box LLMs (2024.findings-acl)
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| Challenge: | Despite the rapid advancements of Large Language Models, the unchecked ultra-large-scale training sets introduce a series of potential risks like data contamination. |
| Approach: | They propose a method to detect contaminated training data and diminish the contamination effect by using a to-be-released dataset. |
| Outcome: | The proposed method outperforms existing methods by at least 4.5% on more 4 dataset formats, with more than 10 base LLMs. |
LongTableBench: Benchmarking Long-Context Table Reasoning across Real-World Formats and Domains (2025.findings-emnlp)
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Liyao Li, Jiaming Tian, Hao Chen, Wentao Ye, Chao Ye, Haobo Wang, Ningtao Wang, Xing Fu, Gang Chen, Junbo Zhao
| Challenge: | Evaluating 52 LLMs reveals that only the strongest models maintain robust performance under increasing context lengths and format diversity. |
| Approach: | They propose a benchmark for evaluating long-context reasoning over semi-structured tables across diverse formats, tasks, and domains. |
| Outcome: | The proposed model outperforms compression-based approaches on tasks requiring semantic integration. |