3 papers
cs.LG2025
DEVAL: A Framework for Evaluating and Improving the Derivation Capability of Large Language Models
Yifan Li, Qin Li, Min Zhang
Assessing the reasoning ability of Large Language Models (LLMs) over data remains an open and pressing research question. Compared with LLMs, human reasoning can derive correspondi…
cs.CL2025
D-SMART: Enhancing LLM Dialogue Consistency via Dynamic Structured Memory And Reasoning Tree
Xiang Lei, Qin Li, Min Zhang
Large Language Models (LLMs) often exhibit factual inconsistencies and logical decay in extended, multi-turn dialogues, a challenge stemming from their reliance on static, pre-trai…
cs.CL2024
ChatSchema: A pipeline of extracting structured information with Large Multimodal Models based on schema
Fei Wang, Yuewen Zheng, Qin Li +3
Objective: This study introduces ChatSchema, an effective method for extracting and structuring information from unstructured data in medical paper reports using a combination of L…