activity
20192026
most citedChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT

149 citations · 218 across the 53 of their papers we have counts for

collaborators
Showing 2025Show all

10 papers · 1 filter

cs.CL2025

SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models

Rui Qi, Zhibo Man, Yufeng Chen +3

Recent developments have enabled Large Language Models (LLMs) to engage in complex reasoning tasks through deep thinking. However, the capacity of reasoning has not been successful…

cs.CL2025

Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning

Xue Zhang, Yunlong Liang, Fandong Meng +5

Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the ``think-then-answer'' paradigm, which enhances both accuracy and inter…

cs.CL2025

CM-Align: Consistency-based Multilingual Alignment for Large Language Models

Xue Zhang, Yunlong Liang, Fandong Meng +4

Current large language models (LLMs) generally show a significant performance gap in alignment between English and other languages. To bridge this gap, existing research typically…

cs.IR2025

Boosting Data Utilization for Multilingual Dense Retrieval

Chao Huang, Fengran Mo, Yufeng Chen +5

Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of di…

cs.CL2025

Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts

Xue Zhang, Yunlong Liang, Fandong Meng +4

Continually expanding new languages for existing large language models (LLMs) is a promising yet challenging approach to building powerful multilingual LLMs. The biggest challenge…

cs.CL2025

A Dual-Space Framework for General Knowledge Distillation of Large Language Models

Xue Zhang, Songming Zhang, Yunlong Liang +4

Knowledge distillation (KD) is a promising solution to compress large language models (LLMs) by transferring their knowledge to smaller models. During this process, white-box KD me…

cs.CL2025

DMDTEval: An Evaluation and Analysis of LLMs on Disambiguation in Multi-domain Translation

Zhibo Man, Yuanmeng Chen, Yujie Zhang +1

Currently, Large Language Models (LLMs) have achieved remarkable results in machine translation. However, their performance in multi-domain translation (MDT) is less satisfactory,…

cs.CV2025

D2C: Unlocking the Potential of Continuous Autoregressive Image Generation with Discrete Tokens

Panpan Wang, Liqiang Niu, Fandong Meng +3

In the domain of image generation, latent-based generative models occupy a dominant status; however, these models rely heavily on image tokenizer. To meet modeling requirements, au…

cs.CL2025

AlignDistil: Token-Level Language Model Alignment as Adaptive Policy Distillation

Songming Zhang, Xue Zhang, Tong Zhang +3

In modern large language models (LLMs), LLM alignment is of crucial importance and is typically achieved through methods such as reinforcement learning from human feedback (RLHF) a…

cs.CL2025

Warmup-Distill: Bridge the Distribution Mismatch between Teacher and Student before Knowledge Distillation

Zengkui Sun, Yijin Liu, Fandong Meng +3

The widespread deployment of Large Language Models (LLMs) is hindered by the high computational demands, making knowledge distillation (KD) crucial for developing compact smaller o…