Grammaticality Representation in ChatGPT as Compared to Linguists and Laypeople

TL;DR

This study investigates whether ChatGPT's grammatical judgments align with those of linguists and laypeople, revealing high convergence and correlations across various judgment tasks, and exploring the nature of LLMs' grammatical intuition.

Contribution

It provides the first large-scale comparison of ChatGPT's grammatical judgments with human judgments, highlighting the extent of its human-like grammatical intuition.

Findings

ChatGPT's judgments converge with linguists at 73-95% rates.

High correlation (around 89%) between ChatGPT and linguists across tasks.

Differences in judgment styles attributed to processing differences.

Abstract

Large language models (LLMs) have demonstrated exceptional performance across various linguistic tasks. However, it remains uncertain whether LLMs have developed human-like fine-grained grammatical intuition. This preregistered study (https://osf.io/t5nes) presents the first large-scale investigation of ChatGPT's grammatical intuition, building upon a previous study that collected laypeople's grammatical judgments on 148 linguistic phenomena that linguists judged to be grammatical, ungrammatical, or marginally grammatical (Sprouse, Schutze, & Almeida, 2013). Our primary focus was to compare ChatGPT with both laypeople and linguists in the judgement of these linguistic constructions. In Experiment 1, ChatGPT assigned ratings to sentences based on a given reference sentence. Experiment 2 involved rating sentences on a 7-point scale, and Experiment 3 asked ChatGPT to choose the more grammatical sentence from a pair. Overall, our findings demonstrate convergence rates ranging from 73% to 95% between ChatGPT and linguists, with an overall point-estimate of 89%. Significant correlations were also found between ChatGPT and laypeople across all tasks, though the correlation strength varied by task. We attribute these results to the psychometric nature of the judgment tasks and the differences in language processing styles between humans and LLMs.