ACCESS：抽象因果事件发现与推理基准
ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning
AI总结：
针对现有NLP事件因果识别方法在分布外场景的不足，提出抽象因果事件发现与推理基准ACCESS，从GLUCOSE提取1.4K抽象因果对，实验显示其可提升LLMs的问答推理性能，同时揭示相关任务的挑战。
摘要：
识别因果关系对于理解现实世界动态并最终实现因果推理至关重要。自然语言处理（NLP）中现有的事件因果识别方法（包括基于大型语言模型（LLMs）的方法）由于现有基准规模有限且严重依赖词汇线索，在分布外场景中表现不佳。受概率因果推理启发的现代基准尝试将事件因果图构建为因果知识的鲁棒表示，其中CRAB是该方向近期的此类基准之一。本文介绍ACCESS，一个专为抽象因果事件发现与推理设计的基准。与现有资源不同，ACCESS聚焦于抽象层面的日常生活事件因果关系。我们提出一种从GLUCOSE（大规模隐式常识因果知识数据集）中识别事件泛化抽象的流程，并从中提取1.4K因果对。实验突出了NLP中使用统计方法和/或LLMs进行自动抽象识别与因果发现的持续挑战，但同时证明ACCESS提供的抽象因果知识可用于提升LLMs的问答推理性能。
英文摘要：
Identifying cause-and-effect relationships is critical to understanding real-world dynamics and ultimately causal reasoning. Existing methods for identifying event causality in NLP, including those based on Large Language Models (LLMs), exhibit difficulties in out-of-distribution settings due to the limited scale and heavy reliance on lexical cues within available benchmarks. Modern benchmarks, inspired by probabilistic causal inference, have attempted to construct causal graphs of events as a robust representation of causal knowledge, where \texttt{CRAB} \citep{romanou2023crab} is one such recent benchmark along this line. In this paper, we introduce \texttt{ACCESS}, a benchmark designed for discovery and reasoning over abstract causal events. Unlike existing resources, \texttt{ACCESS} focuses on causality of everyday life events on the abstraction level. We propose a pipeline for identifying abstractions for event generalizations from \texttt{GLUCOSE} \citep{mostafazadeh-etal-2020-glucose}, a large-scale dataset of implicit commonsense causal knowledge, from which we subsequently extract $1,4$K causal pairs. Our experiments highlight the ongoing challenges of using statistical methods and/or LLMs for automatic abstraction identification and causal discovery in NLP. Nonetheless, we demonstrate that the abstract causal knowledge provided in \texttt{ACCESS} can be leveraged for enhancing QA reasoning performance in LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
