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20232026
most citedAnalyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction

27 citations · 28 across the 6 of their papers we have counts for

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cs.CL2026

How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay Drafts

Steven Coyne, Diana Galvan-Sosa, Ryan Spring +4

This study examines feedback in English as a Foreign Language (EFL) writing contexts, focusing on written corrective feedback (WCF). Large language models (LLMs) can provide WCF at…

cs.CL2025

Teacher Demonstrations in a BabyLM's Zone of Proximal Development for Contingent Multi-Turn Interaction

Suchir Salhan, Hongyi Gu, Donya Rooein +5

Multi-turn dialogues between a child and a caregiver are characterized by a property called contingency - that is, prompt, direct, and meaningful exchanges between interlocutors. W…

cs.CL2025

BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data

Jaap Jumelet, Abdellah Fourtassi, Akari Haga +23

We present BabyBabelLM, a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. We curate developmentally pla…

cs.CL2025★ 1 cited

Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems

Steven Coyne, Diana Galvan-Sosa, Ryan Spring +4

Recent advances in natural language processing (NLP) have contributed to the development of automated writing evaluation (AWE) systems that can correct grammatical errors. However,…

cs.CL2025

Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset

Diana Galvan-Sosa, Gabrielle Gaudeau, Pride Kavumba +5

The performance and usability of Large-Language Models (LLMs) are driving their use in explanation generation tasks. However, despite their widespread adoption, LLM explanations ha…

cs.CL2023★ 27 cited

Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction

Steven Coyne, Keisuke Sakaguchi, Diana Galvan-Sosa +2

GPT-3 and GPT-4 models are powerful, achieving high performance on a variety of Natural Language Processing tasks. However, there is a relative lack of detailed published analysis…