5 citations · 25 across the 33 of their papers we have counts for
14 papers · 1 filter
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…