arXiv Computation and Language

Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation

The paper introduces Cross-Preference Learning (CPL), a training framework that explicitly models the complementary strengths of sentence-level and context-aware machine translation. By incorporating intra- and cross-condition preferences into the optimization objective, CPL provides targeted supervision to leverage useful contextual signals while remaining robust to uninformative context. Experiments on multiple public context-aware MT tasks with models such as Qwen3-4B, Qwen3-8B, and Llama-3-8B-Instruct show consistent improvements in translation quality and robustness without altering the model architecture.

arXiv Computation and Language

Sep 1

Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent

The paper investigates how machine translation can be tailored to specific audiences and intents, a capability enabled by large language models (LLMs). By systematically evaluating purpose-driven MT across 50 languages, 5 model sizes, and 8 text domains, the authors find that explicit instructions significantly improve translation adaptiveness, especially for informal domains, larger models, and higher-resource languages. They also show that traditional MT metrics often penalize adapted translations and that models can self-generate useful instructions from context, closing a large portion of the adaptiveness gap.

By Raphael Merx, Ekaterina Vylomova, Trevor Cohn

llms nlp

arXiv AI

Sep 24

Scaling Attention Head Analysis via Gradient-Based Attribution in Context-Aware Machine Translation

arXiv:2609.28117v1 Announce Type: cross Abstract: In this paper, we introduce a gradient-based head attribution strategy where the Token-level Max-Margin loss is backpropagated to the attention maps....

By Pawe{\l} M\k{a}ka, Yusuf Can Semerci, Jan Scholtes, Gerasimos Spanakis

llms nlp safety

arXiv Machine Learning

Jul 21

Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift

arXiv:2607. 17524v1 Announce Type: cross Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task.

By Zitong Huang, Gustavo Lucas Carvalho, Deqing Fu, Robin Jia

nlp fine-tuning benchmarks

arXiv AI

Sep 15

Balancing Global Quality and Pronoun-Specific Feedback for Context-Aware Machine Translation

The paper introduces ProNMT, a reward-guided iterative self‑training approach that balances global translation quality with pronoun‑specific feedback for context‑aware machine translation. ProNMT samples candidate translations, scores them using reference‑free quality estimation and a pronoun label derived from references, and fine‑tunes on the highest‑scoring candidate. Experiments on English–German Europarl and English–French News Commentary show that ProNMT outperforms standard context‑aware fine‑tuning on BLEU and COMET, while ablations reveal that pronoun‑only feedback can harm overall quality and that confidence‑weighted feedback outperforms hard binary feedback.

By Harshit Dhankhar, Baban Gain, Asif Ekbal, Yogesh Mani Tripathi

nlp fine-tuning

arXiv AI

Jun 2

Understanding LLM Behavior in Multi-Target Cross-Lingual Summarization

arXiv:2606. 01252v1 Announce Type: cross Abstract: Multi-target cross-lingual text summarization (MTXLS), which summarizes a source document into multiple target languages, is increasingly important as users consume content in diverse languages, but remains underexplored.

By Sangwon Ryu, Yihong Liu, Mingyang Wang, Yunsu Kim, Jungseul Ok, Gary Geunbae Lee, Hinrich Schuetze

llms nlp benchmarks

arXiv Computation and Language

Oct 15

Copy-Paste Language Models

arXiv:2610.19247v1 Announce Type: new Abstract: Transformers are intuitively considered to be efficient at copying from their inputs. We actually show that the ability of LLMs to copy in context can...

By Nathan Godey, Yoav Artzi

llms

arXiv Computation and Language

Sep 23

POPI: Personalizing LLMs via Optimized Natural Language Preference Inference

arXiv:2510.17881v4 Announce Type: replace Abstract: Large language models (LLMs) are typically aligned with population-level preferences, despite substantial variation across individual users. We int...

By Yizhuo Chen, Xin Liu, Ruijie Wang, Zheng Li, Pei Chen, Changlong Yu, Qingyu Yin, Priyanka Nigam, Meng Jiang, Bing Yin

llms nlp reinforcement-learning benchmarks

Hugging Face Trending Papers

Jul 20

Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift

We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task. Our key intuition is that by training the model to distinguish good and bad tokens in a response, we naturally guide the model towards generating good tokens, while avoiding the pitfalls that come with directly training the model to generate off-policy tokens.

nlp fine-tuning benchmarks

Hugging Face Trending Papers

Oct 13

Copy-Paste Language Models

Transformers are intuitively considered to be efficient at copying from their inputs. We actually show that the ability of LLMs to copy in context can be improved through explicit modeling. We propose...

llms

arXiv AI

Aug 11

Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning

arXiv:2608. 09507v1 Announce Type: cross Abstract: Natural language user preferences provide an interpretable interface for LLM personalization.

By Yuting Liu, Wei Wu, Jianzhe Zhao, Guibing Guo

llms rag agents reinforcement-learning

arXiv AI

Jun 4

SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization

arXiv:2505. 11166v3 Announce Type: replace-cross Abstract: Despite advances in pretraining with extended context sizes, large language models (LLMs) still face challenges in effectively utilizing real-world long-context information, primarily due to insufficient long-context alignment caused by data quality issues, training inefficiencies, and the lack of well-designed optimization objectives.

By Huashan Sun, Shengyi Liao, Yansen Han, Yu Bai, Yang Gao, Cheng Fu, Weizhou Shen, Fanqi Wan, Ming Yan, Ji Zhang, Fei Huang

llms benchmarks safety data-engineering

arXiv AI

Aug 6

Calibrating Transformer Attention via Task-Space Sensitivity Feedback

arXiv:2512. 20661v2 Announce Type: replace Abstract: Transformer-based pre-trained language models (PLMs) excel in text classification but suffer from attention dilution and attention sink effects, forcing models to over-focus on task-irrelevant tokens.

By Yawei Liu

llms nlp reinforcement-learning benchmarks safety

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