1 citations · 2 across the 10 of their papers we have counts for
11 papers
Retrieval Heads are Dynamic
Yuping Lin, Zitao Li, Yue Xing +6
Recent studies have identified "retrieval heads" in Large Language Models (LLMs) responsible for extracting information from input contexts. However, prior works largely rely on st…
Beyond Data Privacy: New Privacy Risks for Large Language Models
Yuntao Du, Zitao Li, Ninghui Li +1
Large Language Models (LLMs) have achieved remarkable progress in natural language understanding, reasoning, and autonomous decision-making. However, these advancements have also c…
AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
Dawei Gao, Zitao Li, Yuexiang Xie +20
Driven by rapid advancements of Large Language Models (LLMs), agents are empowered to combine intrinsic knowledge with dynamic tool use, greatly enhancing their capacity to address…
FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation
Fatema Siddika, Md Anwar Hossen, J. Pablo Muñoz +3
Parameter-efficient fine-tuning (PEFT) adapts large pre-trained models by updating only a small subset of parameters. Recently, Representation Fine-Tuning (ReFT) has emerged as an…
Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation
Ze Yu Zhang, Zitao Li, Yaliang Li +2
Retrieval-augmented generation (RAG) based on large language models often falters on narrative documents with inherent temporal structures. Standard unstructured RAG methods rely s…
Efficient Leave-one-out Approximation in LLM Multi-agent Debate Based on Introspection
Yue Cui, Liuyi Yao, Zitao Li +3
Multi-agent systems based on large language models (LLMs) advance automatic task completion in various fields, where debate is a common cooperation form for agents to solve complic…
VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy Optimization
Yunxin Li, Xinyu Chen, Zitao Li +5
Applying Reinforcement Learning (RL) to Video Large Language Models (Video-LLMs) shows significant promise for complex video reasoning. However, popular Reinforcement Fine-Tuning (…
Automated Profile Inference with Language Model Agents
Yuntao Du, Zitao Li, Bolin Ding +4
Impressive progress has been made in automated problem-solving by the collaboration of large language model (LLM) based agents. However, these automated capabilities also open aven…
Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
Yunxin Li, Zhenyu Liu, Zitao Li +19
Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains. In artificial intelligence, as systems increasi…
Talk to Right Specialists: Iterative Routing in Multi-agent Systems for Question Answering
Feijie Wu, Zitao Li, Fei Wei +3
Retrieval-augmented generation (RAG) agents are increasingly deployed to answer questions over local knowledge bases that cannot be centralized due to knowledge-sovereignty constra…
A Bargaining-based Approach for Feature Trading in Vertical Federated Learning
Yue Cui, Liuyi Yao, Zitao Li +3
Vertical Federated Learning (VFL) has emerged as a popular machine learning paradigm, enabling model training across the data and the task parties with different features about the…