1 citations · 1 across the 8 of their papers we have counts for
8 papers
Conflict-Aware Multi-Agent Reasoning via Signed Graph Modeling
Longgang He, Longzhu He, Daojing He +2
LLM-based multi-agent systems (MAS) seek to improve reasoning by combining diverse perspectives from multiple agents. However, aggregation may overlook supportive, contradictory, a…
MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus
Yexing Du, Kaiyuan Liu, Bihe Zhang +9
With the rapid advancement of Multimodal Large Language Models (MLLMs), their potential has gained significant attention in Chinese Classical Studies (CCS). While existing research…
Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer
Guodong Du, Zitao Fang, Jing Li +10
Foundation models and their checkpoints have significantly advanced deep learning, boosting performance across various applications. However, fine-tuned models often struggle outsi…
MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teaming
Weiyang Guo, Jing Li, Wenya Wang +4
The proliferation of jailbreak attacks against large language models (LLMs) highlights the need for robust security measures. However, in multi-round dialogues, malicious intention…
Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling
Junlin Li, Guodong DU, Jing Li +8
Fine-tuning Large Language Models (LLMs) with multimodal encoders on modality-specific data expands the modalities that LLMs can handle, leading to the formation of Multimodal LLMs…
ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation
Kaiyuan Liu, Youcheng Pan, Yang Xiang +4
Recently, LLM agents have made rapid progress in improving their programming capabilities. However, existing benchmarks lack the ability to automatically evaluate from users' persp…
Knowledge Editing with Dynamic Knowledge Graphs for Multi-Hop Question Answering
Yifan Lu, Yigeng Zhou, Jing Li +5
Multi-hop question answering (MHQA) poses a significant challenge for large language models (LLMs) due to the extensive knowledge demands involved. Knowledge editing, which aims to…
Parameter Competition Balancing for Model Merging
Guodong Du, Junlin Lee, Jing Li +8
While fine-tuning pretrained models has become common practice, these models often underperform outside their specific domains. Recently developed model merging techniques enable t…