arXiv:2609.21231v1 Announce Type: new Abstract: Reference-based metrics for Grammatical Error Correction (GEC) such as M$^2$ and ERRANT assume that the reference set enumerates all valid edits, and t...
arXiv:2610.15056v1 Announce Type: cross Abstract: Large language models can post-edit ASR by using linguistic context to compare N-best hypotheses and rewrite the transcript when needed, often improv...
arXiv:2606. 00020v1 Announce Type: cross Abstract: Large Language Model (LLM) based Chinese Grammatical Error Correction (CGEC) systems face two critical challenges: general-purpose models lack specialized linguistic priors for subtle grammatical distinctions, and Supervised Fine-Tuning (SFT) with Maximum Likelihood Estimation fails to optimize for precision-focused metrics, leading to systematic over-correction.
arXiv:2609.22793v1 Announce Type: new Abstract: LLM-based machine translation evaluation can closely match human judgments, but in practice it remains largely diagnostic, with the signals rarely tran...
The paper presents a prompt-based method for minimal-edit grammatical error correction (GEC) that reduces overcorrection in large language models (LLMs). It introduces taxonomy-based instructions, batch prompting to regularize overcorrection, and LLM-assisted prompt optimization, achieving an $F_{0.5}$ score of 78.32 on BEA-2019 with Gemini 3.1-Pro. This approach narrows the performance gap to fine-tuned models while avoiding their infrastructure demands.
arXiv:2608.28614v1 Announce Type: cross Abstract: Sequence-to-edit approaches make grammatical error correction (GEC) efficient and locally interpretable by predicting edit labels over the input rath...
arXiv:2609.23573v1 Announce Type: new Abstract: Synthetic essays can help reduce dependence on human-written data in Automated Essay Scoring (AES). However, they often lack realistic errors, limiting...
Chinese Semantic Error Correction (CSEC) targets semantic errors in Chinese text, which are typically more subtle and complex than spelling and grammatical errors but remain relatively underexplored....
arXiv:2609.38384v1 Announce Type: new Abstract: Verified proof edits offer a natural source of supervision for improving language-model-generated Lean proofs. Yet verification establishes that an edi...
The paper examines how large language models (LLMs) can be tuned for grammatical error correction (GEC) by adjusting inference-time parameters. It demonstrates that the repetition penalty can be used to control the precision–recall trade-off during decoding, without changing the model architecture, training, or prompt. Experiments on standard GEC benchmarks confirm that this simple, broadly applicable method allows inference-time adjustment of the precision–recall balance, similar to prior sequence‑to‑sequence techniques such as Align‑and‑Predict Decoding.
Causal Edit Serialization (CES) is a framework that enables autoregressive language models to perform revisions—such as insertion, deletion, replacement, copying, and movement—within the same causal token stream. CES uses special pointer tokens paired with predicted source addresses, a lightweight localization head, and dual-index rotary position encoding to localize edits while preserving standard causal attention. Experiments show that CES models can execute complete edits with only a modest increase in clean-text bits per byte, and that post‑training with supervised or reinforcement learning can produce beneficial grammatical error corrections.
arXiv:2602.20207v4 Announce Type: replace-cross Abstract: Knowledge editing in Large Language Models (LLMs) aims to update the model's prediction for a specific query to a desired target while preser...