Papers with Debiasing and Distillation-Enhanced Supervised Fine-Tuning
PIPER: Benchmarking and Prompting Event Reasoning Boundary of LLMs via Debiasing-Distillation Enhanced Tuning (2025.acl-long)
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Zhicong Lu, Changyuan Tian, PeiguangLi PeiguangLi, Li Jin, Sirui Wang, Wei Jia, Ying Shen, Guangluan Xu
| Challenge: | Existing studies on Large Language Models (LLMs) have failed to evaluate their performance in event reasoning with a single event relational type or reasoning format. |
| Approach: | They propose a benchmark to evaluate LLMs' event reasoning capability using a single event relational type or reasoning format. |
| Outcome: | The proposed model improves on 10K diverse instruction-tuning demonstrations to alleviate event reasoning-oriented data scarcity. |