Abstract:Explaining how to get from A to B can be challenging. It requires mentally simulating what the listener will do based on what they are told. To capture this process, we propose a computational model that converts utterances into action plans: a large language model translates an explanation into program-like guidance (a policy prior and value map), and a planning agent executes it under partial observability. We score explanations by the efficiency and reliability of the resulting paths, penalizing replanning. Across four preregistered experiments, we collect a corpus of 1,200 explanations over 24 maps, elicit helpfulness judgments, measure baseline navigation, and test behavior with explanations of differing quality. Higher-scored explanations are judged more helpful and improve navigation: participants with explanations outperform those without, and high-scoring explanations help more than low-scoring ones. Together, these results show procedural explanation as utility-guided communication shaped by how language can be grounded into action under uncertainty.
| Comments: | CogSci 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.08406 [cs.CL] |
| (or arXiv:2605.08406v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08406 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hanqi Zhou [view email]
[v1]
Fri, 8 May 2026 19:12:14 UTC (1,750 KB)