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...
arXiv:2609.15559v1 Announce Type: cross Abstract: Grammatical error correction (GEC) evaluation has traditionally relied on reference or edit overlap, which can penalize valid rewrites that differ fr...
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:2407. 00740v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are widely adopted in real-world applications, it has become critical to ensure LLMs satisfy safety constraints, such as non-toxicity and logical consistency, as well as task- and situation-specific constraints.
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.
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.
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: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:2609.21231v1 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...
The paper introduces a layered taxonomy for annotating grammatical errors in Chinese learner writing, aiming for consistency and linguistic relevance. It first classifies character- and punctuation-level orthographic errors by edit operation and subtype, then assigns other errors a three-layer core label that combines edit operation, linguistic domain, and part of speech, with optional Chinese-specific extensions. The taxonomy is evaluated through coverage analysis of automatically extracted edits and a preliminary consistency study using five large language models, confirming the layered approach while highlighting areas needing refinement.
arXiv:2607. 08646v1 Announce Type: cross Abstract: As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish.
arXiv:2609.16340v1 Announce Type: cross Abstract: Machine translation systems are periodically upgraded to stronger models, but the available preference signal is human post-edits of an older system'...