5 citations · 25 across the 33 of their papers we have counts for
7 papers · 1 filter
DiaCBT: A Long-Periodic Dialogue Corpus Guided by Cognitive Conceptualization Diagram for CBT-based Psychological Counseling
Yougen Zhou, Ningning Zhou, Qin Chen +3
Psychotherapy reaches only a small fraction of individuals suffering from mental disorders due to social stigma and the limited availability of therapists. Large language models (L…
Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design
Lang Yu, Zhangyang Gao, Cheng Tan +3
SE(3)-based generative models have shown great promise in protein geometry modeling and effective structure design. However, the field currently lacks a modularized benchmark to en…
A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law
Qianjun Pan, Wenkai Ji, Yuyang Ding +8
This survey explores recent advancements in reasoning large language models (LLMs) designed to mimic "slow thinking" - a reasoning process inspired by human cognition, as described…
Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning
Junsong Li, Jie Zhou, Yutao Yang +7
Automatic math correction aims to check students' solutions to mathematical problems via artificial intelligence technologies. Most existing studies focus on judging the final answ…
Code-Driven Inductive Synthesis: Enhancing Reasoning Abilities of Large Language Models with Sequences
Kedi Chen, Zhikai Lei, Fan Zhang +7
Large language models make remarkable progress in reasoning capabilities. Existing works focus mainly on deductive reasoning tasks (e.g., code and math), while another type of reas…
LLM-KT: Aligning Large Language Models with Knowledge Tracing using a Plug-and-Play Instruction
Ziwei Wang, Jie Zhou, Qin Chen +5
The knowledge tracing (KT) problem is an extremely important topic in personalized education, which aims to predict whether students can correctly answer the next question based on…
Enhancing Uncertainty Modeling with Semantic Graph for Hallucination Detection
Kedi Chen, Qin Chen, Jie Zhou +7
Large Language Models (LLMs) are prone to hallucination with non-factual or unfaithful statements, which undermines the applications in real-world scenarios. Recent researches focu…