35 citations · 68 across the 37 of their papers we have counts for
11 papers · 1 filter
Mitigating Strategy Preference Bias in Emotional Support Conversation via Uncertainty Estimations
Yougen Zhou, Qin Chen, Ningning Zhou +3
Emotional support conversation (ESC) aims to alleviate distress through empathetic dialogue, yet large language models (LLMs) face persistent challenges in delivering effective ESC…
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…
Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering
Linhao Ye, Lang Yu, Zhikai Lei +3
Retrieval-augmented generation (RAG) is usually integrated into large language models (LLMs) to mitigate hallucinations and knowledge obsolescence. Whereas,conventional one-step re…
Task-Core Memory Management and Consolidation for Long-term Continual Learning
Tianyu Huai, Jie Zhou, Yuxuan Cai +5
In this paper, we focus on a long-term continual learning (CL) task, where a model learns sequentially from a stream of vast tasks over time, acquiring new knowledge while retainin…
Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback
Yutao Yang, Jie Zhou, Junsong Li +5
This paper introduces an interactive continual learning paradigm where AI models dynamically learn new skills from real-time human feedback while retaining prior knowledge. This pa…
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…
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering
Tianyu Huai, Jie Zhou, Xingjiao Wu +4
Multimodal large language models (MLLMs) have garnered widespread attention from researchers due to their remarkable understanding and generation capabilities in visual language ta…
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…