Document-level Event Causality Identification (DECI) aims to identify causal
relations between two events in documents. Recent research tends to use
pre-trained language models to generate the event causal relations. Whereas,
these methods are prone to the errors of sequential generation due to multiple
events in a document. Moreover, the potential structures such as event
coreference and related causal chain are neglected. In this paper, we propose a
multi-task learning framework to enhance event causality identification with
rationale and structure-aware causal question answering. Specifically, the DECI
task is transformed into multiple-choice question answering, and the causes and
effects of the questioned event are generated with large language models. In
addition, we generate the rationales to explain why these events have causal
relations. Moreover, we construct an event structure graph, which models the
multi-hop potential relations for causal reasoning of the current event.
Experiments on two benchmark datasets show the great advantages of our proposed
approach compared to the state-of-the-art methods. Moreover, we conduct both
quantitative and qualitative analyses, which shed light on why each component
of our approach can lead to great improvements.

提出了一种多任务学习框架，通过因果性问题回答和事件结构图，增强了文档级事件因果性识别，并通过生成理由和结构感知的因果性问答来解释具有因果关系的事件。

用根据和结构的因果问答提升事件因果性鉴定

Enhancing Event Causality Identification with Rationale and  Structure-Aware Causal Question Answering

While Large Language Models (LLMs) have found success in real-world
applications, their underlying explanatory process is still poorly understood.
This paper proposes IBE-Eval, a framework inspired by philosophical accounts on
Inference to the Best Explanation (IBE) to advance the interpretation and
evaluation of LLMs' explanations. IBE-Eval estimates the plausibility of
natural language explanations through a combination of explicit logical and
linguistic features including: consistency, parsimony, coherence, and
uncertainty. Extensive experiments are conducted on Causal Question Answering
(CQA), where \textit{IBE-Eval} is tasked to select the most plausible causal
explanation amongst competing ones generated by LLMs (i.e., GPT 3.5 and Llama
2). The experiments reveal that IBE-Eval can successfully identify the best
explanation with up to 77\% accuracy ($\approx 27\%$ above random), improving
upon a GPT 3.5-as-a-Judge baseline ($\approx+17\%$) while being intrinsically
more efficient and interpretable. Additional analyses suggest that, despite
model-specific variances, LLM-generated explanations tend to conform to IBE
criteria and that IBE-Eval is significantly correlated with human judgment,
opening up opportunities for future development of automated explanation
verification tools.

利用 IBE-Eval 框架对大型语言模型（LLMs）的解释进行评估，通过考虑一致性、简洁性、连贯性和不确定性等逻辑和语言特征，从中选择最可信的解释，并成功识别出最佳解释的可能性高达 77％（约高于随机 27％），同时更高效和可解释。此方法还与人类判断存在显著相关性，为未来发展自动解释验证工具提供了机会。

大型语言模型中的最佳解释推理

Inference to the Best Explanation in Large Language Models

Causal questions inquire about causal relationships between different events
or phenomena. Specifically, they often aim to determine whether there is a
relationship between two phenomena, or to identify all causes/effects of a
phenomenon. Causal questions are important for a variety of use cases,
including virtual assistants and search engines. However, many current
approaches to causal question answering cannot provide explanations or evidence
for their answers. Hence, in this paper, we aim to answer causal questions with
CauseNet, a large-scale dataset of causal relations and their provenance data.
Inspired by recent, successful applications of reinforcement learning to
knowledge graph tasks, such as link prediction and fact-checking, we explore
the application of reinforcement learning on CauseNet for causal question
answering. We introduce an Actor-Critic based agent which learns to search
through the graph to answer causal questions. We bootstrap the agent with a
supervised learning procedure to deal with large action spaces and sparse
rewards. Our evaluation shows that the agent successfully prunes the search
space to answer binary causal questions by visiting less than 30 nodes per
question compared to over 3,000 nodes by a naive breadth-first search. Our
ablation study indicates that our supervised learning strategy provides a
strong foundation upon which our reinforcement learning agent improves. The
paths returned by our agent explain the mechanisms by which a cause produces an
effect. Moreover, for each edge on a path, CauseNet stores its original source
on the web allowing for easy verification of paths.

本研究利用强化学习在 CauseNet 知识图谱上应用 Actor-Critic 方式，通过搜索图谱回答因果问题，并通过经验学习和算法改进降低搜索空间，结果表明与朴素的宽度优先搜索相比，每个二元因果问题访问不到 30 个节点，在路径上的边均有原始来源，可轻松验证。