CxMP: A Linguistic Minimal-Pair Benchmark for Evaluating Constructional Understanding in Language Models
Miyu Oba, Saku Sugawara
Abstract
Recent work has examined language models from a linguistic perspective to better understand how they acquire language. Most existing benchmarks focus on judging grammatical acceptability, whereas the ability to interpret meanings conveyed by grammatical forms has received much less attention. We introduce the Linguistic Minimal-Pair Benchmark for Evaluating Constructional Understanding in Language Models (CxMP), a benchmark grounded in Construction Grammar that treats form-meaning pairings, or constructions, as fundamental linguistic units. CxMP evaluates whether models can interpret the semantic relations implied by constructions, using a controlled minimal-pair design across nine construction types, including the let-alone, caused motion, and ditransitive constructions. Our results show that while syntactic competence emerges early, constructional understanding develops more gradually and remains limited even in large language models (LLMs). CxMP thus reveals persistent gaps in how language models integrate form and meaning, providing a framework for studying constructional understanding and learning trajectories in language models.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 5c94fe10-a71d-44ea-8044-cb91d1832cc6Builds on5
- Masked Language Model ScoringJulian Salazar, Davis Liang, Toan Q. Nguyen, Katrin KirchhoffACL 2020 · 167 citations
- Mission: Impossible Language ModelsJulie Kallini, Isabel Papadimitriou, Richard Futrell, Kyle Mahowald et al.ACL 2024 · 15 citations
- The better your Syntax, the better your Semantics? Probing Pretrained Language Models for the English Comparative CorrelativeLeonie Weissweiler, Valentin Hofmann, Abdullatif Köksal, Hinrich SchützeEMNLP 2022 · 13 citations
- Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNsKanishka Misra, Kyle MahowaldEMNLP 2024 · 11 citations
- Developmentally-plausible Working Memory Shapes a Critical Period for Language AcquisitionMasato Mita, Ryo Yoshida, Yohei OsekiACL 2025 · 7 citations
Related papers
- Unpacking Let Alone: Human-Scale Models Generalize to a Rare Construction in Form but not MeaningWesley Scivetti, Tatsuya Aoyama, Ethan Wilcox, Nathan SchneiderEMNLP 2025
- TurBLiMP: A Turkish Benchmark of Linguistic Minimal PairsEzgi Basar, Francesca Padovani, Jaap Jumelet, Arianna BisazzaEMNLP 2025
- An Investigation of LLMs' Inefficacy in Understanding Converse RelationsChengwen Qi, Bowen Li, Binyuan Hui, Bailin Wang et al.EMNLP 2023 · 4 citations
- The BLA Benchmark: Investigating Basic Language Abilities of Pre-Trained Multimodal ModelsXinyi Chen, Raquel Fernández, Sandro PezzelleEMNLP 2023
- CoELM: Construction-Enhanced Language ModelingLvxiaowei Xu, Zhilin Gong, Jianhua Dai, Tianxiang Wang et al.ACL 2024
