Conflicts in Texts: Data, Implications and Challenges (2504.19472v1)

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.

Conflicts in Texts: Data, Implications and Challenges

The paper "Conflicts in Texts: Data, Implications and Challenges" presents a thorough exploration of conflicting information within texts as encountered in NLP. Authored by Siyi Liu and Dan Roth, the paper identifies and categorizes conflicting information that can impact the efficacy and trustworthiness of NLP models.

Overview

The paper delineates conflicts into three main categories: those originating from natural texts on the web, those emerging from human-annotated data, and those appearing in model interactions. Each of these categories presents distinct challenges that can affect the reliability of NLP applications.

1. Natural Texts on the Web: Conflicts in web data arise primarily due to factual inconsistencies, biases, and multiple perspectives. Such inconsistencies are prevalent in open-domain question answering (QA) systems and retrieval-augmented generation systems. The paper identifies semantic ambiguities and contradictory evidence from multiple sources as key contributors to these conflicts.
2. Human-Annotated Data: Annotator disagreement and biases are a significant source of conflict in human-annotated datasets. Subjective judgments often lead to inconsistent labeling, affecting tasks like sentiment analysis and hate speech detection. Societal biases related to race and ethnicity can seep into annotations, leading to skewed training data and subsequent model predictions.
3. Model Interactions: During deployment, models can hallucinate and produce outputs that contradict established facts. Knowledge conflicts also arise between stored parametric knowledge and external contextual inputs, presenting challenges for LLMs in maintaining consistency and factual accuracy.

Implications and Challenges

The paper underscores the importance of addressing these conflicts to build reliable and robust NLP systems. Conflicts, if ignored, can undermine model performance and trustworthiness, especially in scenarios that demand high accuracy and reliability.

• Factual Conflicts: These are especially challenging in open-domain QA systems, where models have to reconcile conflicting information from varied sources. The failure to accurately handle such conflicts can severely impact the reliability of answers provided by the system.
• Opinion Disagreements: Multi-perspective analyses are vital for tasks such as summarization and dialogue generation in the presence of biased data. Maintaining neutrality and coherence in these tasks is critical for achieving fair and balanced NLP outputs.
• Hallucinations and Knowledge Conflicts: Models often overly depend on memorized knowledge, leading to hallucinations and misinformation in responses. Strategies such as retrieval augmentation and fact-checking mechanisms are necessary to mitigate such issues.

Speculations on Future Developments

Looking forward, the paper speculates that future research will likely focus on developing conflict-aware systems capable of nuanced reasoning over conflicting information. This includes:

• Adaptive Mechanisms: Developing adaptive mechanisms in NLP models to reconcile differences between contextual inputs and stored knowledge.
• Bias Mitigation: Implementing techniques to reduce societal and demographic biases in annotations, which can distort model predictions.
• Enhanced Retrieval Methods: Improving retrieval methods to better filter and present diverse perspectives and factual data without contradictions.

Conclusion

The exploration provided by Liu and Roth offers comprehensive insight into the multifaceted nature of conflicts in NLP systems. Their work prompts necessary discourse on building robust, trustworthy, and equitable AI systems capable of processing and integrating conflicting information effectively.