# Leveraging Human Production-Interpretation Asymmetries to Test LLM Cognitive Plausibility

arXiv: https://arxiv.org/abs/2503.17579 · HTML: https://mlsanity.com/paper/2503.17579/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 21 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning](https://mlsanity.com/paper/2312.01552/) (2023)
- [Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State](https://mlsanity.com/paper/1903.03260/) (2019)
- [A fine-grained comparison of pragmatic language understanding in humans and language models](https://mlsanity.com/paper/2212.06801/) (2022)
- [One fish, two fish, but not the whole sea: Alignment reduces language models' conceptual diversity](https://mlsanity.com/paper/2411.04427/) (2024)
- [How Linguistics Learned to Stop Worrying and Love the Language Models](https://mlsanity.com/paper/2501.17047/) (2025)
- [Language models align with human judgments on key grammatical constructions](https://mlsanity.com/paper/2402.01676/) (2024)
- [Large Language Models Are Human-Like Internally](https://mlsanity.com/paper/2502.01615/) (2025)
- [Large Language Models Are Partially Primed in Pronoun Interpretation](https://mlsanity.com/paper/2305.16917/) (2023)

## Built on by (most cited first, 2 of 2)

- [Production and Perception in LLMs: A Token Probability Approach](https://mlsanity.com/paper/2607.11703/) (2026)
- [Reading Without a Reader: Large Language Models Collapse Reading and Writing into a Single Entangled Code](https://mlsanity.com/paper/2607.24797/) (2026)

## Abstract (arXiv)

Whether large language models (LLMs) process language similarly to humans has been the subject of much theoretical and practical debate. We examine this question through the lens of the production-interpretation distinction found in human sentence processing and evaluate the extent to which instruction-tuned LLMs replicate this distinction. Using an empirically documented asymmetry between pronoun production and interpretation in humans for implicit causality verbs as a testbed, we find that some LLMs do quantitatively and qualitatively reflect human-like asymmetries between production and interpretation. We demonstrate that whether this behavior holds depends upon both model size-with larger models more likely to reflect human-like patterns and the choice of meta-linguistic prompts used to elicit the behavior. Our codes and results are available at https://github.com/LingMechLab/Production-Interpretation_Asymmetries_ACL2025.