Accurate but Natural? Diagnosing Grammatical and Idiomatic Gaps in Japanese EFL Writing
- URL: http://arxiv.org/abs/2608.09289v1
- Date: Mon, 10 Aug 2026 08:42:06 GMT
- Title: Accurate but Natural? Diagnosing Grammatical and Idiomatic Gaps in Japanese EFL Writing
- Abstract summary: This study introduces a layered LLM-correction pipeline that isolates structural errors from unnaturalness.<n>Applying the overused-based CEFR-J grammar extractor, we quantify two diagnostic measures: accuracy gaps and idiomatic gaps.<n>Results reveal distinct patterns: definite articles, third-person singular -s, and modals (would, could) exhibit significant accuracy difficulties.<n>A two-dimensional instructional typology maps error rates against idiomatic gaps, distinguishing accurate but grammar items from error-prone or avoided complex forms.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Second language writing research distinguishes grammatical accuracy from native-like idiomaticity, yet automated writing evaluation often conflates these dimensions. This study introduces a layered LLM-correction pipeline that isolates structural errors from unnaturalness by generating literal error corrections and idiomatic revisions for 3,830 English writing samples from 120 Japanese junior high school students. Applying the regex-based CEFR-J grammar extractor, we quantify two diagnostic measures: accuracy gaps (structures attempted but incorrectly produced) and idiomatic gaps (grammatically correct structures underused or overused relative to native norms). Results reveal distinct patterns: definite articles, third-person singular -s, and modals (would, could) exhibit significant accuracy difficulties, while -ing forms and hypothetical modals (would) show the largest idiomatic underuse, with simple present verbs, subject-verb-object patterns, and modal can conversely exhibiting the most pronounced overuse. A two-dimensional instructional typology maps error rates against idiomatic gaps, distinguishing accurate but overused grammar items from error-prone or avoided complex forms requiring targeted production practice. This framework advances pedagogical feedback by enabling teachers to diagnose whether learner difficulties arise from inaccurate execution, structural avoidance, or L1-mapped overreliance, supporting evidence-based interventions tailored to the specific needs of each learner.
Related papers
- LLM Reasoning Predicts When Models Are Right: Evidence from Coding Classroom Discourse [0.18268488712787334]
Large Language Models (LLMs) are increasingly deployed to automatically label and analyze educational dialogue at scale.<n>We investigate whether reasoning generated by LLMs can be used to predict the correctness of a model's own predictions.<n>We analyze 30,300 teacher utterances from classroom dialogue, each labeled by multiple state-of-the-art LLMs with an instructional move construct and an accompanying reasoning.
arXiv Detail & Related papers (2026-02-10T14:38:13Z) - Grammaticality Judgments in Humans and Language Models: Revisiting Generative Grammar with LLMs [0.0]
In traditional generative grammar, systematic contrasts in grammaticality such as subject-auxiliary inversion and the licensing of parasitic gaps are taken as evidence for an internal, hierarchical grammar.<n>We test whether large language models (LLMs), trained only on surface forms, reproduce these contrasts in ways that imply an underlying structural representation.
arXiv Detail & Related papers (2025-12-11T09:17:35Z) - Do What? Teaching Vision-Language-Action Models to Reject the Impossible [53.40183895299108]
Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks.<n>We propose Instruct-Verify-and-Act (IVA), a framework that detects when an instruction cannot be executed due to a false premise.<n>Our experiments show that IVA improves false premise detection accuracy by 97.56% over baselines.
arXiv Detail & Related papers (2025-08-22T10:54:33Z) - Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems [25.047364950581265]
We introduce an annotation framework that models each error's error type and generalizability.<n>We collect a dataset of annotated learner errors and corresponding human-written feedback comments.<n>We evaluate keyword-guided, keyword-free, and template-guided methods of generating feedback.
arXiv Detail & Related papers (2025-08-09T04:06:18Z) - Inconsistent Tokenizations Cause Language Models to be Perplexed by Japanese Grammar [27.3347020320559]
We measure the perplexity of language models when confronted with the "first person psych predicate restriction" grammar point in Japanese.<n>We show in further experiments that language models will use alternative grammar patterns in order to produce grammatical sentences when tokenization issues prevent the most natural sentence from being output.
arXiv Detail & Related papers (2025-05-26T07:08:47Z) - Subtle Errors in Reasoning: Preference Learning via Error-injected Self-editing [59.405145971637204]
We propose a novel preference learning framework called eRror-Injected Self-Editing (RISE)<n>RISE injects predefined subtle errors into pivotal tokens in reasoning or steps to construct hard pairs for error mitigation.<n>Experiments validate the effectiveness of RISE, with preference learning on Qwen2-7B-Instruct yielding notable improvements of 3.0% on GSM8K and 7.9% on MATH with only 4.5K training samples.
arXiv Detail & Related papers (2024-10-09T07:43:38Z) - Towards preserving word order importance through Forced Invalidation [80.33036864442182]
We show that pre-trained language models are insensitive to word order.
We propose Forced Invalidation to help preserve the importance of word order.
Our experiments demonstrate that Forced Invalidation significantly improves the sensitivity of the models to word order.
arXiv Detail & Related papers (2023-04-11T13:42:10Z) - Improving Pre-trained Language Models with Syntactic Dependency
Prediction Task for Chinese Semantic Error Recognition [52.55136323341319]
Existing Chinese text error detection mainly focuses on spelling and simple grammatical errors.
Chinese semantic errors are understudied and more complex that humans cannot easily recognize.
arXiv Detail & Related papers (2022-04-15T13:55:32Z) - Extracting Grammars from a Neural Network Parser for Anomaly Detection
in Unknown Formats [79.6676793507792]
Reinforcement learning has recently shown promise as a technique for training an artificial neural network to parse sentences in some unknown format.
This paper presents procedures for extracting production rules from the neural network, and for using these rules to determine whether a given sentence is nominal or anomalous.
arXiv Detail & Related papers (2021-07-30T23:10:24Z) - On the Robustness of Language Encoders against Grammatical Errors [66.05648604987479]
We collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data.
Results confirm that the performance of all tested models is affected but the degree of impact varies.
arXiv Detail & Related papers (2020-05-12T11:01:44Z) - Limits of Detecting Text Generated by Large-Scale Language Models [65.46403462928319]
Some consider large-scale language models that can generate long and coherent pieces of text as dangerous, since they may be used in misinformation campaigns.
Here we formulate large-scale language model output detection as a hypothesis testing problem to classify text as genuine or generated.
arXiv Detail & Related papers (2020-02-09T19:53:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.