Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model Evaluation (2024.acl-long)
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| Challenge: | Recent advances in large language models have improved performance across tasks . however, the sensitivity of LLMs to prompt leads to unreliability of evaluation results . |
| Approach: | They propose a new concept to evaluate language models with a fixed question or limited paraphrases as the query. |
| Outcome: | The proposed method outperforms existing benchmarks on multiple language models . it avoids prompt sensitivity, rendering models more reliable and robust . |
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Jiao Sun, Yufei Tian, Wangchunshu Zhou, Nan Xu, Qian Hu, Rahul Gupta, John Wieting, Nanyun Peng, Xuezhe Ma
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KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models (2025.findings-emnlp)
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Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao, Zhuo Chen, Guangyu Li, Feiteng Mu, Mengting Hu, Pengjun Xie, Fei Huang
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