Technology Category
Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)Machine Learning: Large Multimodal Models (LMMs)Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
Conflicts in Texts: Data, Implications and Challenges
🤖 AI Summary
NLP models often suffer from reduced reliability and trustworthiness in practice due to reliance on or generation of conflicting information—arising from factual contradictions and subjective biases in natural text, annotator disagreements and societal biases in training data, and hallucinations or knowledge inconsistencies during model interaction. This paper provides the first unified taxonomy of conflicts across the entire NLP pipeline, introduces a cross-scenario conflict classification framework and an extensible mitigation paradigm, and fills a critical gap in systematic surveys on conflict-aware modeling. By integrating semantic consistency analysis, annotation robustness evaluation, generation credibility calibration, and multi-perspective reasoning, we establish a human-in-the-loop mechanism for conflict detection and resolution. The work clarifies the fundamental impact of conflicts on model trustworthiness, identifies six core challenges, and outlines future research directions—thereby offering both theoretical foundations and practical guidelines for developing interpretable, debuggable, and trustworthy NLP systems.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Addressing conflicting information in NLP models
Categorizing conflicts in natural texts and annotations
Developing strategies to mitigate model reliability issues
Innovation
Methods, ideas, or system contributions that make the work stand out.
Categorize text conflicts into three key areas
Unify conflicts under broader information concept
Propose mitigation strategies for conflict-aware NLP
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