Work

CxMP: A Linguistic Minimal-Pair Benchmark for Evaluating Constructional Understanding in Language Models

Year: 2026

Type: conference-paper

Abstract: Understanding language acquisition in language models remains an open question, yet many benchmarks focus on grammatical acceptability, with far less attention to interpreting meanings conveyed by grammatical forms.We introduce the Linguistic Minimal-Pair Benchmark for Evaluating Constructional Understanding in Language Models (CxMP), grounded in Construction Grammar, which treats form-meaning pairings (constructions) as fundamental linguistic units.It evaluates whether models interpret the semantic information implied by constructions, using a controlled minimal-pairs across nine types.Our results show that constructional understanding develops more gradually and remains limited for some constructions even in large language models (LLMs), whereas performance on grammatical acceptability emerges earlier, with shallow heuristics in CxMP exhibiting a U-shaped pattern.These findings highlight the need to broaden existing linguistic evaluations to capture meanings encoded in linguistic form. 1

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2026.acl-long.2132

Authors: Miyu Oba, Saku Sugawara

Institutions: Nara Institute of Science and Technology, National Institute of Informatics, The University of Tokyo

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Japan Society for the Promotion of Science

Award: 生得原理の探究による自然な言語獲得モデルの構築