2 citations · 2 across the 11 of their papers we have counts for
12 papers
KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models
Songming Zhang, Xue Zhang, Tong Zhang +3
Knowledge distillation (KD) is widely used to compress and post-train large language models (LLMs), yet many existing frameworks execute teacher inference with the same training-or…
Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation
Rui Qi, Fengran Mo, Yufeng Chen +6
Multilingual retrieval-augmented generation (MRAG) requires models to effectively acquire and integrate beneficial external knowledge from multilingual collections. However, most e…
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…
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…
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…
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…
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…
Dual-Space Knowledge Distillation for Large Language Models
Songming Zhang, Xue Zhang, Zengkui Sun +2
Knowledge distillation (KD) is known as a promising solution to compress large language models (LLMs) via transferring their knowledge to smaller models. During this process, white…
Multilingual Knowledge Editing with Language-Agnostic Factual Neurons
Xue Zhang, Yunlong Liang, Fandong Meng +4
Multilingual knowledge editing (MKE) aims to simultaneously update factual knowledge across multiple languages within large language models (LLMs). Previous research indicates that…
TransportationGames: Benchmarking Transportation Knowledge of (Multimodal) Large Language Models
Xue Zhang, Xiangyu Shi, Xinyue Lou +4
Large language models (LLMs) and multimodal large language models (MLLMs) have shown excellent general capabilities, even exhibiting adaptability in many professional domains such…
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase Generation
Xue Zhang, Songming Zhang, Yunlong Liang +4
Existing syntactically-controlled paraphrase generation (SPG) models perform promisingly with human-annotated or well-chosen syntactic templates. However, the difficulty of obtaini…
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation
Siyu Lai, Zhen Yang, Fandong Meng +4
Generating adversarial examples for Neural Machine Translation (NMT) with single Round-Trip Translation (RTT) has achieved promising results by releasing the meaning-preserving res…