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October 3, 2025Open Access

It Depends: Resolving Referential Ambiguity in Minimal Contexts with Commonsense Knowledge

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Current large language models struggle with referential ambiguity, committing to single interpretations instead of exploring options.
Testing various models revealed that simplification requests significantly reduced the effectiveness of commonsense reasoning.
Fine-tuning the model Llama-3.1-8B showed substantial improvements in resolving ambiguity in conversations.
An understanding of how these models react under varying contexts is crucial for improving their communication capabilities.

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This analysis demonstrates how large language models handle referential ambiguity, suggesting fine-tuning can enhance peers' handling of context.

Ellinger et al. (2025) studied this question.

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This analysis demonstrates how large language models handle referential ambiguity, suggesting fine-tuning can enhance peers' handling of context.