@inproceedings{turker-eryigit-2026-instruction,
    title = "Instruction-Following {LLM}s for Grammatical Error Correction: Analyzing Neutral-Anchored Instructional Sensitivity Across Editing Modes",
    author = {T{\"u}rker, Tolgahan  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en},
    editor = "Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bann{\`o}, Stefano  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Anais  and
      Yaneva, Victoria  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 21st Workshop on Innovative Use of {NLP} for Building Educational Applications ({BEA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bea-1.17/",
    pages = "234--247",
    ISBN = "979-8-89176-409-5",
    abstract = "Grammatical Error Correction (GEC) requires models to make edit decisions under competing objectives: correcting errors while either minimizing changes or maximizing fluency.However, we lack a principled characterization of how instruction-following Large Language Models (LLMs) shift their edit decisions across such editing modes, and whether standard evaluation setups faithfully reflect these shifts.We address this gap by defining three modes{---}Neutral, Minimal-Edit, and Fluency-Edit{---}and measuring neutral-anchored performance shifts to quantify instructional sensitivity.We benchmark seven LLMs, including proprietary and open-weight models, in a unified zero-shot prompting schema on CoNLL-2014, BEA-2019, and JFLEG datasets.The Minimal-Edit instruction mitigates over-editing and typically boosts precision; in some settings, strong models also improve recall, suggesting more selective and effective corrections.In contrast, the Fluency-Edit instruction often encourages broader paraphrastic rewriting that may improve perceived fluency while lowering GLEU, suggesting both a metric-objective mismatch and a shift away from targeted local correction.Notably, Claude-Sonnet-4.5 demonstrates superior zero-shot capabilities, outperforming previously reported scores and matching or even exceeding few-shot results across CoNLL-2014 (F{\_}0.5: 67.05), BEA-2019 (F{\_}0.5: 64.91), and JFLEG (GLEU: 66.09)."
}