A Dual-Task Paradigm to Investigate Sentence Comprehension Strategies in Language Models
Rei Emura, Saku Sugawara

TL;DR
This paper introduces a dual-task paradigm combining arithmetic and sentence comprehension to study how resource constraints influence language models' strategies, revealing a shift towards plausibility-based inference similar to humans.
Contribution
It presents a novel dual-task experimental setup to investigate the impact of cognitive resource limitations on language models' comprehension strategies.
Findings
Models shift towards plausibility-based comprehension under dual-task constraints.
Models show increased accuracy gap between plausible and implausible sentences in dual-task conditions.
Results support that resource limitations promote rational inference in language models.
Abstract
Language models (LMs) behave more like humans when their cognitive resources are restricted, particularly in predicting sentence processing costs such as reading times. However, it remains unclear whether such constraints similarly affect sentence comprehension strategies. Besides, existing methods do not directly target the balance between memory storage and sentence processing, which is central to human working memory. To address this issue, we propose a dual-task paradigm that combines an arithmetic computation task with a sentence comprehension task, such as "The 2 cocktail + blended 3 =..." Our experiments show that under dual-task conditions, GPT-4o, o3-mini, and o4-mini shift toward plausibility-based comprehension, mirroring humans' rational inference. Specifically, these models show a greater accuracy gap between plausible sentences (e.g., "The cocktail was blended by the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
