探究大模型的因果推理能力及其在NLP中的应用
Causality for Natural Language Processing
- 构建新数据集与评估框架，测试大模型的因果推理水平
- 发现大模型在因果任务中表现有限，且存在反因果学习偏差
- 适用于对大模型可解释性与决策可信度感兴趣的研究者
因果推理是人类智能的核心，也是实现高级理解与决策的人工系统的关键能力。本论文从多个维度探讨大型语言模型（LLMs）中的因果推理与理解能力，涵盖对模型因果推断技能的系列研究、其性能背后的作用机制，以及因果与反因果学习在自然语言处理任务中的影响。同时，研究将因果推理应用于基于文本的计算社会科学，聚焦政治决策与引文评价中的科学影响力分析。通过创新的数据集、基准任务与方法框架，本文识别出提升大模型因果能力的关键挑战与机遇，为该领域的未来发展提供了全面基础。
原文摘要 · Abstract (English)
Causal reasoning is a cornerstone of human intelligence and a critical capability for artificial systems aiming to achieve advanced understanding and decision-making. This thesis delves into various dimensions of causal reasoning and understanding in large language models (LLMs). It encompasses a series of studies that explore the causal inference skills of LLMs, the mechanisms behind their performance, and the implications of causal and anticausal learning for natural language processing (NLP) tasks. Additionally, it investigates the application of causal reasoning in text-based computational social science, specifically focusing on political decision-making and the evaluation of scientific impact through citations. Through novel datasets, benchmark tasks, and methodological frameworks, this work identifies key challenges and opportunities to improve the causal capabilities of LLMs, providing a comprehensive foundation for future research in this evolving field.
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