4 papers
GUI-AC: Enhancing Continual Learning in GUI Agents
Can Lin, Tao Feng, Hangjie Yuan +3
Graphical User Interfaces (GUIs) serve as the dominant medium for human-computer interaction, yet building GUI agents that generalize across the vast diversity of real-world interf…
5% > 100%: Flatness Preference is All You Need for Multimodal Parameter-Efficient Fine-Tuning
Yifan Zhu, Can Lin, Hangjie Yuan +4
Parameter-Efficient Fine-Tuning (PEFT) methods provide a streamlined and efficient tool for adapting large models to domain-specific multimodal downstream tasks. Although these met…
GraphIF: Enhancing Multi-Turn Instruction Following for Large Language Models with Relation Graph Prompt
Zhenhe Li, Can Lin, Ling Zheng +3
Multi-turn instruction following is essential for building intelligent conversational systems that can consistently adhere to instructions across dialogue turns. However, existing…
RJE: A Retrieval-Judgment-Exploration Framework for Efficient Knowledge Graph Question Answering with LLMs
Can Lin, Zhengwang Jiang, Ling Zheng +4
Knowledge graph question answering (KGQA) aims to answer natural language questions using knowledge graphs. Recent research leverages large language models (LLMs) to enhance KGQA r…