most citedLearning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning Distractor

3 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

cs.AI2025

Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree

Qi Peng, Jialin Cui, Jiayuan Xie +2

Large language models (LLMs) have shown great potential in the medical domain. However, existing models still fall short when faced with complex medical diagnosis task in the real…

cs.CV2025

ExpStar: Towards Automatic Commentary Generation for Multi-discipline Scientific Experiments

Jiali Chen, Yujie Jia, Zihan Wu +6

Experiment commentary is crucial in describing the experimental procedures, delving into underlying scientific principles, and incorporating content-related safety guidelines. In p…

cs.CV2025

CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction

Jiali Chen, Xusen Hei, HongFei Liu +5

Computer-aided design (CAD) is crucial in prototyping 3D objects through geometric instructions (i.e., CAD programs). In practical design workflows, designers often engage in time-…

cs.CL2025

Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model

Jiali Chen, Xusen Hei, Yuqi Xue +3

Chinese literary classics hold significant cultural and educational value, offering deep insights into morality, history, and human nature. These works often include classical Chin…

cs.CV20243 cited

Learning to Correction: Explainable Feedback Generation for Visual Commonsense Reasoning Distractor

Jiali Chen, Xusen Hei, Yuqi Xue +4

Large multimodal models (LMMs) have shown remarkable performance in the visual commonsense reasoning (VCR) task, which aims to answer a multiple-choice question based on visual com…