Hallucination Detection for Grounded Instruction Generation

Lingjun Zhao; Khanh Nguyen; Hal Daumé III

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arXiv · 2310.15319

Hallucination Detection for Grounded Instruction Generation

Lingjun Zhao·Khanh Nguyen·Hal Daumé III

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Abstract

We investigate the problem of generating instructions to guide humans to navigate in simulated residential environments. A major issue with current models is hallucination: they generate references to actions or objects that are inconsistent with what a human follower would perform or encounter along the described path. We develop a model that detects these hallucinated references by adopting a model pre-trained on a large corpus of image-text pairs, and fine-tuning it with a contrastive loss that separates correct instructions from instructions containing synthesized hallucinations. Our final model outperforms several baselines, including using word probability estimated by the instruction-generation model, and supervised models based on LSTM and Transformer.

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Lingjun Zhao, Khanh Nguyen, Hal Daumé III. 2023-10-23. Hallucination Detection for Grounded Instruction Generation. https://arxiv.org/abs/2310.15319

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