activity
20152025
most citedMultilingual Machine Translation with Large Language Models: Empirical Results and Analysis

55 citations · 375 across the 77 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.CL2025

SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models

Peng Ding, Wen Sun, Dailin Li +4

Large Language Models (LLMs) excel at various natural language processing tasks but remain vulnerable to jailbreaking attacks that induce harmful content generation. In this paper,…

cs.CL2025

Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancement

Peng Ding, Jun Kuang, Zongyu Wang +4

Large Language Models (LLMs) have shown impressive capabilities across various tasks but remain vulnerable to meticulously crafted jailbreak attacks. In this paper, we identify a c…

cs.AI2025

The First Impression Problem: Internal Bias Triggers Overthinking in Reasoning Models

Renfei Dang, Zhening Li, Shujian Huang +1

Reasoning models often exhibit overthinking, characterized by redundant reasoning steps. We identify \emph{internal bias} elicited by the input question as a key trigger of such be…

cs.CL2025

Understanding LLMs' Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From

Changjiang Gao, Hankun Lin, Xin Huang +5

Cross-lingual context retrieval (extracting contextual information in one language based on requests in another) is a fundamental aspect of cross-lingual alignment, but the perform…

cs.CL2025

Generalizing From Short to Long: Effective Data Synthesis for Long-Context Instruction Tuning

Wenhao Zhu, Pinzhen Chen, Hanxu Hu +4

Long-context modelling for large language models (LLMs) has been a key area of recent research because many real world use cases require reasoning over longer inputs such as docume…

cs.CL2025

Extend Adversarial Policy Against Neural Machine Translation via Unknown Token

Wei Zou, Shujian Huang, Jiajun Chen

Generating adversarial examples contributes to mainstream neural machine translation~(NMT) robustness. However, popular adversarial policies are apt for fixed tokenization, hinderi…