5 citations · 25 across the 33 of their papers we have counts for
33 papers
Can Heterogeneous Language Models Be Fused?
Shilian Chen, Jie Zhou, Qin Chen +4
Model merging aims to integrate multiple expert models into a single model that inherits their complementary strengths without incurring the inference-time cost of ensembling. Rece…
Ask Only When Needed: Proactive Retrieval from Memory and Skills for Experience-Driven Lifelong Agents
Yuxuan Cai, Wei Li, Jie Zhou +4
Online lifelong learning agents must decide not only how to act but also when to consult prior experience to continually improve on long-horizon tasks. Existing methods typically r…
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…
CMM-Math: A Chinese Multimodal Math Dataset To Evaluate and Enhance the Mathematics Reasoning of Large Multimodal Models
Wentao Liu, Qianjun Pan, Yi Zhang +7
Large language models (LLMs) have obtained promising results in mathematical reasoning, which is a foundational skill for human intelligence. Most previous studies focus on improvi…
Boosting Large Language Models with Socratic Method for Conversational Mathematics Teaching
Yuyang Ding, Hanglei Hu, Jie Zhou +3
With the introduction of large language models (LLMs), automatic math reasoning has seen tremendous success. However, current methods primarily focus on providing solutions or usin…
P-React: Synthesizing Topic-Adaptive Reactions of Personality Traits via Mixture of Specialized LoRA Experts
Yuhao Dan, Jie Zhou, Qin Chen +2
Personalized large language models (LLMs) have attracted great attention in many applications, such as emotional support and role-playing. However, existing works primarily focus o…
Modeling Comparative Logical Relation with Contrastive Learning for Text Generation
Yuhao Dan, Junfeng Tian, Jie Zhou +4
Data-to-Text Generation (D2T), a classic natural language generation problem, aims at producing fluent descriptions for structured input data, such as a table. Existing D2T works m…
Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis
Xuanwen Ding, Jie Zhou, Liang Dou +4
Aspect-based sentiment analysis (ABSA) is an important subtask of sentiment analysis, which aims to extract the aspects and predict their sentiments. Most existing studies focus on…
Recent Advances of Foundation Language Models-based Continual Learning: A Survey
Yutao Yang, Jie Zhou, Xuanwen Ding +5
Recently, foundation language models (LMs) have marked significant achievements in the domains of natural language processing (NLP) and computer vision (CV). Unlike traditional neu…
Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check
Linhao Ye, Zhikai Lei, Jianghao Yin +3
Retrieval-Augmented Generation (RAG) aims to generate more reliable and accurate responses, by augmenting large language models (LLMs) with the external vast and dynamic knowledge.…
Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering
Baiyan Zhang, Qin Chen, Jie Zhou +2
Document-level Event Causality Identification (DECI) aims to identify causal relations between two events in documents. Recent research tends to use pre-trained language models to…
Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking
Yiyang Gu, Yougen Zhou, Qin Chen +4
Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection. Recent work focuses on combining task-oriented dialog…
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models
Kedi Chen, Qin Chen, Jie Zhou +2
Since large language models (LLMs) achieve significant success in recent years, the hallucination issue remains a challenge, numerous benchmarks are proposed to detect the hallucin…
A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder
Kedi Chen, Jie Zhou, Qin Chen +2
Information extraction (IE) aims to extract complex structured information from the text. Numerous datasets have been constructed for various IE tasks, leading to time-consuming an…
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis
Zhenxiao Cheng, Jie Zhou, Wen Wu +2
Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) due to their high fidelity. Such methods determine word-lev…
Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models
Shunyu Liu, Jie Zhou, Qunxi Zhu +4
Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text. However, a notable challenge in…
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis
Siyin Wang, Jie Zhou, Qin Chen +3
Domain adaption has been widely adapted for cross-domain sentiment analysis to transfer knowledge from the source domain to the target domain. Whereas, most methods are proposed un…
MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA
Lang Yu, Qin Chen, Jie Zhou +1
Large language models (LLMs) have shown great success in various Natural Language Processing (NLP) tasks, whist they still need updates after deployment to fix errors or keep pace…