35 citations · 68 across the 37 of their papers we have counts for
27 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
Mathematical Language Models: A Survey
Wentao Liu, Hanglei Hu, Jie Zhou +8
In recent years, there has been remarkable progress in leveraging Language Models (LMs), encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models (LLMs), wit…
EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education
Yuhao Dan, Zhikai Lei, Yiyang Gu +13
EduChat (https://www.educhat.top/) is a large-scale language model (LLM)-based chatbot system in the education domain. Its goal is to support personalized, fair, and compassionate…
Tell Model Where to Attend: Improving Interpretability of Aspect-Based Sentiment Classification via Small Explanation Annotations
Zhenxiao Cheng, Jie Zhou, Wen Wu +2
Gradient-based explanation methods play an important role in the field of interpreting complex deep neural networks for NLP models. However, the existing work has shown that the gr…
An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning
Shaobin Chen, Jie Zhou, Yuling Sun +1
Unsupervised sentence embeddings learning has been recently dominated by contrastive learning methods (e.g., SimCSE), which keep positive pairs similar and push negative pairs apar…
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck
Jie Zhou, Qi Zhang, Qin Chen +2
Event argument extraction (EAE) aims to extract arguments with given roles from texts, which have been widely studied in natural language processing. Most previous works have achie…
Causal Intervention Improves Implicit Sentiment Analysis
Siyin Wang, Jie Zhou, Changzhi Sun +4
Despite having achieved great success for sentiment analysis, existing neural models struggle with implicit sentiment analysis. This may be due to the fact that they may latch onto…
CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction
Jiaju Lin, Qin Chen, Jie Zhou +2
Implicit event argument extraction (EAE) aims to identify arguments that could scatter over the document. Most previous work focuses on learning the direct relations between argume…