2 citations · 5 across the 33 of their papers we have counts for
19 papers · 1 filter
A Survey on Collaborating Small and Large Language Models for Performance, Cost-effectiveness, Cloud-edge Privacy, and Trustworthiness
Fali Wang, Jihai Chen, Shuhua Yang +4
Large language models (LLMs) have achieved remarkable progress across domains and applications but face challenges such as high fine-tuning costs, inference latency, limited edge d…
Unlocking the Power of Multi-Agent LLM for Reasoning: From Lazy Agents to Deliberation
Zhiwei Zhang, Xiaomin Li, Yudi Lin +8
Large Language Models (LLMs) trained with reinforcement learning and verifiable rewards have achieved strong results on complex reasoning tasks. Recent work extends this paradigm t…
Generalizing Test-time Compute-optimal Scaling as an Optimizable Graph
Fali Wang, Jihai Chen, Shuhua Yang +7
Test-Time Scaling (TTS) improves large language models (LLMs) by allocating additional computation during inference, typically through parallel, sequential, or hybrid scaling. Howe…
A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications
Minhua Lin, Zongyu Wu, Zhichao Xu +6
The advent of large language models (LLMs) has transformed information access and reasoning through open-ended natural language interaction. However, LLMs remain limited by static…
Seeing but Not Believing: Probing the Disconnect Between Visual Attention and Answer Correctness in VLMs
Zhining Liu, Ziyi Chen, Hui Liu +9
Vision-Language Models (VLMs) achieve strong results on multimodal tasks such as visual question answering, yet they can still fail even when the correct visual evidence is present…
AgentTTS: Large Language Model Agent for Test-time Compute-optimal Scaling Strategy in Complex Tasks
Fali Wang, Hui Liu, Zhenwei Dai +8
Test-time scaling (TTS) enhances the performance of large language models (LLMs) by allocating additional compute resources during inference. However, existing research primarily i…
Bradley-Terry and Multi-Objective Reward Modeling Are Complementary
Zhiwei Zhang, Hui Liu, Xiaomin Li +10
Reward models trained on human preference data have demonstrated strong effectiveness in aligning Large Language Models (LLMs) with human intent under the framework of Reinforcemen…
SUA: Stealthy Multimodal Large Language Model Unlearning Attack
Xianren Zhang, Hui Liu, Delvin Ce Zhang +4
Multimodal Large Language Models (MLLMs) trained on massive data may memorize sensitive personal information and photos, posing serious privacy risks. To mitigate this, MLLM unlear…
To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems
Pengfei He, Zhenwei Dai, Xianfeng Tang +9
Large Language Model-based Multi-Agent Systems (LLM-MAS) have demonstrated strong capabilities in solving complex tasks but remain vulnerable when agents receive unreliable message…
EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association
Weiqi Wang, Limeng Cui, Xin Liu +14
Goal-oriented script planning, or the ability to devise coherent sequences of actions toward specific goals, is commonly employed by humans to plan for typical activities. In e-com…
Harnessing the Unseen: The Hidden Influence of Intrinsic Knowledge in Long-Context Language Models
Yu Fu, Haz Sameen Shahgir, Hui Liu +3
Recent advances in long-context language models (LCLMs), designed to handle extremely long contexts, primarily focus on utilizing external contextual information, often leaving the…
A General Framework to Enhance Fine-tuning-based LLM Unlearning
Jie Ren, Zhenwei Dai, Xianfeng Tang +7
Unlearning has been proposed to remove copyrighted and privacy-sensitive data from Large Language Models (LLMs). Existing approaches primarily rely on fine-tuning-based methods, wh…
Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-Commerce
Jingying Zeng, Zhenwei Dai, Hui Liu +6
Prompting LLMs offers an efficient way to guide output generation without explicit model training. In the e-commerce domain, prompting-based applications are widely used for tasks…
Cite Before You Speak: Enhancing Context-Response Grounding in E-commerce Conversational LLM-Agents
Jingying Zeng, Hui Liu, Zhenwei Dai +5
With the advancement of conversational large language models (LLMs), several LLM-based Conversational Shopping Agents (CSA) have been developed to help customers smooth their onlin…
Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning
Tianci Liu, Ruirui Li, Yunzhe Qi +8
Large language models (LLMs) have achieved remarkable performance on various natural language tasks. However, they are trained on static corpora and their knowledge can become outd…
GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation
Jie He, Jennifer Neville, Mengting Wan +6
Large Language Models (LLMs) can enhance their capabilities as AI assistants by integrating external tools, allowing them to access a wider range of information. While recent LLMs…
How Far are LLMs from Real Search? A Comprehensive Study on Efficiency, Completeness, and Inherent Capabilities
Minhua Lin, Hui Liu, Xianfeng Tang +7
Search plays a fundamental role in problem-solving across various domains, with most real-world decision-making problems being solvable through systematic search. Drawing inspirati…
Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models
Yingqian Cui, Pengfei He, Jingying Zeng +11
Chain-of-Thought (CoT) reasoning, which breaks down complex tasks into intermediate reasoning steps, has significantly enhanced the performance of large language models (LLMs) on c…
Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning
Haoyu Han, Yaochen Xie, Hui Liu +8
Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks; however, they still encounter challenges in reasoning tasks that require understandi…