@inproceedings{lu-etal-2025-piper,
    title = "{PIPER}: Benchmarking and Prompting Event Reasoning Boundary of {LLM}s via Debiasing-Distillation Enhanced Tuning",
    author = "Lu, Zhicong  and
      Tian, Changyuan  and
      PeiguangLi, PeiguangLi  and
      Jin, Li  and
      Wang, Sirui  and
      Jia, Wei  and
      Shen, Ying  and
      Xu, Guangluan",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1389/",
    pages = "28591--28613",
    ISBN = "979-8-89176-251-0",
    abstract = "While Large Language Models (LLMs) excel in diverse domains, their validity in event reasoning remains underexplored. Most existing works merely stagnate at assessing LLMs' event reasoning with a single event relational type or reasoning format, failing to conduct a complete evaluation and provide a practical solution for capability enhancement. In this paper, we propose $\textbf{PIPER}$, the first comprehensive benchmark for $\textbf{P}$robing $\textbf{I}$nto the $\textbf{P}$erformance boundary of LLMs in $\textbf{E}$vent $\textbf{R}$easoning. Motivated by our evaluation observations and error patterns analysis, we meticulously craft 10K diverse instruction-tuning demonstrations to alleviate event reasoning-oriented data scarcity. Additionally, a novel $\textbf{D}$ebiasing and $\textbf{D}$istillation-$\textbf{E}$nhanced $\textbf{S}$upervised $\textbf{F}$ine-$\textbf{T}$uning ($\mathbf{D^2}$$\textbf{E-SFT}$) strategy is presented, which facilitates adhering to context and fixating significant contextual event information to elevate the event reasoning capability. Specifically, $\mathrm{D^2}$E-SFT removes the given sample{'}s context to construct an imagined sample, subtracting its logits to mitigate the bias of neglecting context and improve contextual faithfulness. To guide the model in emphasizing significant contextual event information, $\mathrm{D^2}$E-SFT employs a context-refined sample to achieve self-distillation with the alignment of logits. Extensive experimental results demonstrate the effectiveness of our data and strategy in expanding the performance boundary of event reasoning."
}