11 citations · 20 across the 14 of their papers we have counts for
16 papers
Federated Reasoning Distillation Framework with Model Learnability-Aware Data Allocation
Wei Guo, Siyuan Lu, Xiangdong Ran +8
Data allocation plays a critical role in federated large language model (LLM) and small language models (SLMs) reasoning collaboration. Nevertheless, existing data allocation metho…
Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning
Jiahui Zhou, Dan Li, Boxin Li +6
Time series is a pervasive data type across various application domains, rendering the reasonable solving of diverse time series tasks a long-standing goal. Recent advances in larg…
Entropy-Guided Token Dropout: Training Autoregressive Language Models with Limited Domain Data
Jiapeng Wang, Yiwen Hu, Yanzipeng Gao +7
As access to high-quality, domain-specific data grows increasingly scarce, multi-epoch training has become a practical strategy for adapting large language models (LLMs). However,…
When More Retrieval Hurts: Retrieval-Augmented Code Review Generation
Qianru Meng, Xiao Zhang, Zhaochen Ren +1
Code review generation can reduce developer effort by producing concise, reviewer-style feedback for a given code snippet or code change. However, generation-only models often prod…
Merge and Guide: Unifying Model Merging and Guided Decoding for Controllable Multi-Objective Generation
Guofu Xie, Chen Zhang, Xiao Zhang +3
Adapting to diverse user needs at test time is a key challenge in controllable multi-objective generation. Existing methods are insufficient: merging-based approaches provide indir…
CAPO: Towards Enhancing LLM Reasoning through Generative Credit Assignment
Guofu Xie, Yunsheng Shi, Hongtao Tian +2
Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning abilities of Large Language Models (LLMs) by using rule-based binary feedback. However, current RLV…
LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph
Tu Ao, Yanhua Yu, Yuling Wang +7
Large Language Models (LLMs) have impressive capabilities in text understanding and zero-shot reasoning. However, delays in knowledge updates may cause them to reason incorrectly o…
DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models
Jianyu Liu, Hangyu Guo, Ranjie Duan +14
Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data, thereby introducing new dimensions of potential attacks…
VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search
Xiao Zhang, Guanyu Chen, Boyang Zuo +4
Query-to-bidword(i.e., bidding keyword) rewriting is fundamental to sponsored search, transforming noisy user queries into semantically relevant and commercially valuable keywords.…
Multidimensional Consistency Improves Reasoning in Language Models
Huiyuan Lai, Xiao Zhang, Malvina Nissim
While Large language models (LLMs) have proved able to address some complex reasoning tasks, we also know that they are highly sensitive to input variation, which can lead to diffe…
Bone Soups: A Seek-and-Soup Model Merging Approach for Controllable Multi-Objective Generation
Guofu Xie, Xiao Zhang, Ting Yao +1
User information needs are often highly diverse and varied. A key challenge in current research is how to achieve controllable multi-objective generation while enabling rapid adapt…
PROFIT: A Specialized Optimizer for Deep Fine Tuning
Anirudh S Chakravarthy, Shuai Kyle Zheng, Xin Huang +4
The fine-tuning of pre-trained models has become ubiquitous in generative AI, computer vision, and robotics. Although much attention has been paid to improving the efficiency of fi…
GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs
Advik Raj Basani, Xiao Zhang
LLMs have shown impressive capabilities across various natural language processing tasks, yet remain vulnerable to input prompts, known as jailbreak attacks, carefully designed to…
Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation
Guanhua Chen, Wenhan Yu, Xiao Lu +3
While Retrieval-Augmented Generation (RAG) plays a crucial role in the application of Large Language Models (LLMs), existing retrieval methods in knowledge-dense domains like law a…
AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks
Yifan Zeng, Yiran Wu, Xiao Zhang +2
Despite extensive pre-training in moral alignment to prevent generating harmful information, large language models (LLMs) remain vulnerable to jailbreak attacks. In this paper, we…
How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization
Hai Zhang, Hang Yu, Junqiao Zhao +5
Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high couplin…