6 papers
FOCUS: Decoupling Expert Personas in LLMs to Enhance Domain Expert Capabilities
Guanyu Wang, Zidi Zhang, Xu Chu
Large Language Models (LLMs) can exhibit diverse personas, and activating expert personas has been shown to improve domain expertise and task accuracy. However, existing persona co…
Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization
Xu Chu, Guanyu Wang, Zhijie Tan +4
Large Language Models (LLMs) suffer from order bias, where their performance is affected by the arrangement order of input elements. This unfairness limits the model's applications…
Stepwise Schema-Guided Prompting Framework with Parameter Efficient Instruction Tuning for Multimedia Event Extraction
Xiang Yuan, Xinrong Chen, Haochen Li +4
Multimedia Event Extraction (MEE) has become an important task in information extraction research as news today increasingly prefers to contain multimedia content. Current MEE work…
GuiLoMo: Allocating Expert Number and Rank for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors
Hengyuan Zhang, Xinrong Chen, Yingmin Qiu +7
Parameter-efficient fine-tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), offer an efficient way to adapt large language models with reduced computational costs. How…
Qwen Look Again: Guiding Vision-Language Reasoning Models to Re-attention Visual Information
Xu Chu, Xinrong Chen, Guanyu Wang +5
Inference time scaling drives extended reasoning to enhance the performance of Vision-Language Models (VLMs), thus forming powerful Vision-Language Reasoning Models (VLRMs). Howeve…
Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains
Xu Chu, Zhijie Tan, Hanlin Xue +3
Large Language Models (LLMs) are widely applied to downstream domains. However, current LLMs for high-stakes domain tasks, such as financial investment and legal QA, typically gene…