Hugging Face Trending Papers
Augmenting Text to Increase Translation Difficulty
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arXiv AI
5d ago
Augmenting Text to Increase Translation Difficulty
arXiv:2608. 15932v1 Announce Type: new Abstract: As state-of-the-art machine translation models saturate standard benchmarks, the field needs more challenging evaluations to distinguish between models of varying quality.
arXiv Machine Learning
Jul 22
Translation as Augmentation: Effect of Translated Data on Assessment of Difficulty
arXiv:2607. 19101v1 Announce Type: cross Abstract: Reliable Text Difficulty Assessment is a prerequisite for valid text simplification workflows and personalized learning applications.
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.
arXiv AI
Jun 9
Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation
arXiv:2606. 08011v1 Announce Type: cross Abstract: Although directly prompting off-the-shelf Large Language Models (LLMs) to generate meaning-preserving source rewrites can effectively enhance Machine Translation (MT) quality, doing so requires manually tuning prompts for different MT models.
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.
arXiv AI
Jul 17
LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets
arXiv:2607. 14101v1 Announce Type: cross Abstract: Generating high-quality adversarial texts with low query budgets remains a challenging problem in the hard-label scenario.