---
title: "Open-access Dataset on Acceptability Ratings of — Shin 2026"
description: "Most work on whether language models mirror human linguistic judgements targets English."
authors: ["Gyu-Ho Shin", "Soo-Hwan Lee", "Chanyoung Lee"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.23/"
doi: "10.63317/2icd7h29b849"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/shin-2026-open-access-dataset-acceptability-ratings/"
---
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# Open-access Dataset on Acceptability Ratings of Korean Clausal Constructions by Humans and GPT Models

**Authors:** Gyu-Ho Shin, [Soo-Hwan Lee](https://md.research.elamur.ai/free/en/authors/soo-hwan-lee/), Chanyoung Lee · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.23/) · [PDF](https://aclanthology.org/2026.lrec-1.23.pdf) · [DOI 10.63317/2icd7h29b849](https://doi.org/10.63317/2icd7h29b849) · [OpenAlex](https://openalex.org/W7160084538)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Most work on whether language models mirror human linguistic judgements targets English. Korean has had no openly available set of human sentence ratings comparable to SyntaxGym. The authors address this with a released resource covering three Korean clausal phenomena where morphosyntax meets semantics:
- the dative case alternation,
- active versus passive voice, crossed with theme animacy,
- licensing of the NPI amwuto by negation under the clausemate condition.

They built controlled sentence sets and gathered acceptability judgements from adult native speakers in their twenties. They paired these with sentence-level surprisal from GPT-2 and GPT-Trinity, plus prompted ratings with short justifications from ChatGPT. This tests a linking hypothesis: can model outputs stand in for human judgement?

Alignment turned out to depend on both model and construction. Models followed structurally driven contrasts such as the dative alternation more closely than semantically driven ones like passive animacy or NPI licensing. Surprisal correlated only weakly with human ratings. The dataset and code give a starting point for evaluating LLMs beyond English.

## Key points
- The stimuli comprise 224 sentences: 96 dative (48 Dative–Accusative, 48 Accusative–Accusative), 64 active/passive (2×2 animacy × voice, 16 per cell) and 64 NPI (2×2 NPI location × negation location, 16 per cell). Ten native speakers normed the materials before the main study.
- Models compared with human ratings: GPT-2 and GPT-Trinity (sentence surprisal) and ChatGPT (prompted ratings with one-sentence rationales).
- ChatGPT reproduced the main human contrasts. It gave Dative–Accusative a mean of about 4.56 versus 1.92 for Accusative–Accusative, and it rated grammatical NPI conditions above ungrammatical ones. It showed only a weak animacy effect within passives.
- Correlations with human ratings were small and construction-specific:
  - Dative–Accusative: ChatGPT r ≈ 0.34, GPT-2 r ≈ 0.12.
  - Passives: no reliable surprisal correlations.
  - NPI items: only scattered GPT-Trinity effects.
- All data and code are on OSF (doi:10.17605/OSF.IO/RQZU3). Stated limitations are the modest size and the variability between ChatGPT prompters.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{shin2026open,
  title = {{Open-access Dataset on Acceptability Ratings of Korean Clausal Constructions by Humans and GPT Models}},
  author = {Shin, Gyu-Ho and Lee, Soo-Hwan and Lee, Chanyoung},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/2icd7h29b849},
  url = {https://doi.org/10.63317/2icd7h29b849}
}
```

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