Zero-Shot Belief: A Hard Problem for LLMs

John Murzaku; Owen Rambow

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arXiv · 2502.08777

Zero-Shot Belief: A Hard Problem for LLMs

John Murzaku·Owen Rambow

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Abstract

We present two LLM-based approaches to zero-shot source-and-target belief prediction on FactBank: a unified system that identifies events, sources, and belief labels in a single pass, and a hybrid approach that uses a fine-tuned DeBERTa tagger for event detection. We show that multiple open-sourced, closed-source, and reasoning-based LLMs struggle with the task. Using the hybrid approach, we achieve new state-of-the-art results on FactBank and offer a detailed error analysis. Our approach is then tested on the Italian belief corpus ModaFact.

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John Murzaku, Owen Rambow. 2025-02-12. Zero-Shot Belief: A Hard Problem for LLMs. https://arxiv.org/abs/2502.08777

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