CxMP: A Linguistic Minimal-Pair Benchmark for Evaluating Constructional Understanding in Language Models
Organizations: Nara Institute of Science and Technology · National Institute of Informatics · The University of Tokyo
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.
Figures & tables
| Construction | Constructional Example | Diagnostic Sentence |
| Let-alone | He can’t help N1, let alone N2. | Helping N2/*N1 is more difficult than helping N1/*N2. |
| Helping N1/*N2 is easier than helping N2/*N1. | ||
| Causative-with | N1 covered N2 with a coat. | The one who provided the coat is N1/*N2. |
| The one who received the coat is N2/*N1. | ||
| Way-manner | N1 pushed his way into the room. | He did so while moving/*staying. |
| He did so with/*without effort. |
| Model | Model sz. | Data sz. | Obj. | Score |
| RoBERTa-base | 125M | 160G | MLM | 0.573 |
| GPT-2-medium | 345M | 40G | CLM | 0.537 |
| RoBERTa-large | 355M | 160G | MLM | 0.674 |
| Pythia-410m | 410M | 825G | CLM | 0.544 |
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
| Construction | #Items | Maj. (%) | Agr. (%) |
| Let_alone | 112 | 97.32 | 66.07 |
| Causative_with | 112 | 97.32 | 91.96 |
| Way_manner | 56 | 98.21 | 80.36 |
| CC | 112 | 100.00 | 91.96 |
| Conative | 56 | 67.86 | 44.64 |
| Ditransitive | 112 | 100.00 | 89.29 |
LLM Evaluation
Large language model evaluation measures what models can do, how reliably they do it, and whether benchmark scores predict real-world performance.
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