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About this paper
Siyi Liu, Dan Roth
Siyi Liu and Dan Roth from the University of Pennsylvania and Oracle AI present a comprehensive survey that unifies and categorizes diverse forms of conflicting information in Natural Language Processing (NLP). The work details the origins, implications, and mitigation strategies for conflicts found in natural texts, human-annotated data, and during model interactions, advocating for the development of conflict-aware AI systems capable of tailored responses.
Key insights
- Conflicts in NLP are pervasive and stem from diverse sources including inherent ambiguity, human subjectivity, societal biases in data, and model-specific knowledge issues, requiring a unified understanding.
- LLMs exhibit behaviors like confirmation bias when faced with contradictory evidence and often reflect Western-centric perspectives, leading to inconsistencies and poor generalization across diverse cultures and languages.
- A key pathway to more robust NLP is the development of 'conflict-aware AI systems' that can not only detect but also classify conflicts and respond appropriately, such as asking clarifying questions or presenting multiple perspectives.
Summary by alphaXiv.
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