自然语言处理的因果性
Causality for Natural Language Processing
AI总结：
本论文系统研究大型语言模型中的因果推理能力，涵盖机制、因果/反因果学习及计算社会科学应用，通过新数据集和基准识别挑战，为提升LLM因果能力奠定基础。
摘要：
因果推理是人类智能的基石，也是旨在实现高级理解和决策的人工系统的关键能力。本论文深入探讨了大型语言模型（LLMs）中因果推理与理解的多个维度。它包含一系列研究，探索LLMs的因果推断技能、其表现背后的机制，以及因果和反因果学习对自然语言处理（NLP）任务的影响。此外，它还研究了因果推理在基于文本的计算社会科学中的应用，特别关注政治决策和通过引文评估科学影响力。通过新颖的数据集、基准任务和方法论框架，这项工作识别了提升LLMs因果能力的关键挑战与机遇，为该不断发展的领域中的未来研究提供了全面基础。
英文摘要：
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.
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。
