As NLP models become increasingly integrated into real-world applications, it becomes clear that there is a need to address the fact that models often rely on and generate conflicting information. Conflicts could reflect the complexity of situations, changes that need to be explained and dealt with, difficulties in data annotation, and mistakes in generated outputs. In all cases, disregarding the conflicts in data could result in undesired behaviors of models and undermine NLP models' reliability and trustworthiness. This survey categorizes these conflicts into three key areas: (1) natural texts on the web, where factual inconsistencies, subjective biases, and multiple perspectives introduce contradictions; (2) human-annotated data, where annotator disagreements, mistakes, and societal biases impact model training; and (3) model interactions, where hallucinations and knowledge conflicts emerge during deployment. While prior work has addressed some of these conflicts in isolation, we unify them under the broader concept of conflicting information, analyze their implications, and discuss mitigation strategies. We highlight key challenges and future directions for developing conflict-aware NLP systems that can reason over and reconcile conflicting information more effectively.

翻译：随着自然语言处理模型日益融入现实世界应用，模型常常依赖并生成冲突信息的问题亟待解决。冲突可能反映情境的复杂性、需要解释和处理的变化、数据标注的困难以及生成输出的错误。在所有情况下，忽视数据中的冲突都可能导致模型产生不良行为，并损害自然语言处理模型的可靠性与可信度。本综述将这些冲突归纳为三个关键领域：(1) 网络自然文本中，事实不一致性、主观偏见与多元视角引发的矛盾；(2) 人工标注数据中，标注者分歧、错误与社会偏见对模型训练的影响；(3) 模型交互过程中，部署时产生的幻觉现象与知识冲突。尽管已有研究分别探讨了部分冲突类型，但我们将它们统一于冲突信息这一更广义的概念框架下，系统分析其影响并讨论缓解策略。最后，我们重点阐述了开发具备冲突感知能力的自然语言处理系统的关键挑战与未来方向，这类系统应能更有效地对冲突信息进行推理与协调。