80 citations · 121 across the 25 of their papers we have counts for
27 papers
MOCC-R1: Reinforcing Reasoning-Response Consistency for Multimodal Counselor Response Generation
Wenjie Zheng, Qiming Xie, Jianfei Yu +1
Multimodal counselor response generation (MCRG) aims to generate an appropriate counselor response from multimodal dialogue histories. Progress is limited by two gaps: first, exist…
FARCA: Fact-Aligned Reliability-Aware Credit Assignment for Reinforcement Learning with Factual Supervision
Qiming Xie, Wenjie Zheng, Xiangqing Shen +1
To reduce the hallucination risk caused by outcome-driven rewards in large language models trained through reinforcement learning with verifiable rewards, existing mitigation appro…
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Yuhang Wu, Xiangqing Shen, Fanfan Wang +4
Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typically optimized on static human annotated…
Execution-Verified Reinforcement Learning for Optimization Modeling
Runda Guan, Xiangqing Shen, Jiajun Zhang +3
Automating optimization modeling with LLMs is a promising path toward scalable decision intelligence, but existing approaches either rely on agentic pipelines built on closed-sourc…
ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains
Zilu Dong, Xiangqing Shen, Zinong Yang +1
Current knowledge editing methods for large language models (LLMs) struggle to maintain logical consistency when propagating ripple effects to associated facts. We propose ChainEdi…
Reason-Align-Respond: Aligning LLM Reasoning with Knowledge Graphs for KGQA
Xiangqing Shen, Fanfan Wang, Rui Xia
LLMs have demonstrated remarkable capabilities in complex reasoning tasks, yet they often suffer from hallucinations and lack reliable factual grounding. Meanwhile, knowledge graph…
LatentCRS: A Variational EM Framework for Bridging Semantics and Behavior in LLM-based Conversational Recommendation
Guanrong Li, Kuo Tian, Jinnan Qi +4
Conversational Recommender Systems (CRS) powered by Large Language Models (LLMs) enable users to articulate explicit and dynamic preferences, overcoming the limitations of fixed te…
Neutralizing Popularity Bias in LLM-based Recommendation via Counterfactual Reasoning Guidelines
Guanrong Li, Haolin Yang, Xinyu Liu +3
In the era of generative AI, recommender systems are moving from precise prediction to trustworthy generation. Large language models (LLMs) support this shift by inferring user int…
MEMIT-Merge: Addressing MEMIT's Key-Value Conflicts in Same-Subject Batch Editing for LLMs
Zilu Dong, Xiangqing Shen, Rui Xia
As large language models continue to scale up, knowledge editing techniques that modify models' internal knowledge without full retraining have gained significant attention. MEMIT,…
Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarization
Wenjie Zheng, Qiming Xie, Jianfei Yu +6
Recent advances in multimodal depression recognition for clinical interviews (MDRC) have demonstrated the potential of AI systems by integrating textual, acoustic, and facial cues.…
Advancing Grounded Multimodal Named Entity Recognition via LLM-Based Reformulation and Box-Based Segmentation
Jinyuan Li, Ziyan Li, Han Li +4
Grounded Multimodal Named Entity Recognition (GMNER) task aims to identify named entities, entity types and their corresponding visual regions. GMNER task exhibits two challenging…
SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations
Fanfan Wang, Heqing Ma, Jianfei Yu +2
The ability to understand emotions is an essential component of human-like artificial intelligence, as emotions greatly influence human cognition, decision making, and social inter…
VCD: A Dataset for Visual Commonsense Discovery in Images
Xiangqing Shen, Fanfan Wang, Siwei Wu +1
Visual commonsense plays a vital role in understanding and reasoning about the visual world. While commonsense knowledge bases like ConceptNet provide structured collections of gen…
MathPile: A Billion-Token-Scale Pretraining Corpus for Math
Zengzhi Wang, Xuefeng Li, Rui Xia +1
High-quality, large-scale corpora are the cornerstone of building foundation models. In this work, we introduce MathPile, a diverse and high-quality math-centric corpus comprising…
In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models
Yunlong Chen, Yaming Zhang, Jianfei Yu +2
Knowledge Base Question Answering (KBQA) aims to answer factoid questions based on knowledge bases. However, generating the most appropriate knowledge base query code based on Natu…
A New Dialogue Response Generation Agent for Large Language Models by Asking Questions to Detect User's Intentions
Siwei Wu, Xiangqing Shen, Rui Xia
Large Language Models (LLMs), such as ChatGPT, have recently been applied to various NLP tasks due to its open-domain generation capabilities. However, there are two issues with ap…
Ask Again, Then Fail: Large Language Models' Vacillations in Judgment
Qiming Xie, Zengzhi Wang, Yi Feng +1
We observe that current conversational language models often waver in their judgments when faced with follow-up questions, even if the original judgment was correct. This wavering…
MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis
Hongjie Cai, Nan Song, Zengzhi Wang +7
Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple…
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering
Siwei Wu, Xiangqing Shen, Rui Xia
The nodes in the commonsense knowledge graph (CSKG) are normally represented by free-form short text (e.g., word or phrase). Different nodes may represent the same concept. This le…
Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study
Zengzhi Wang, Qiming Xie, Yi Feng +3
Recently, ChatGPT has drawn great attention from both the research community and the public. We are particularly interested in whether it can serve as a universal sentiment analyze…
Personality-aware Human-centric Multimodal Reasoning: A New Task, Dataset and Baselines
Yaochen Zhu, Xiangqing Shen, Rui Xia
Personality traits, emotions, and beliefs shape individuals' behavioral choices and decision-making processes. However, for one thing, the affective computing community normally fo…
UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning
Zengzhi Wang, Rui Xia, Jianfei Yu
Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level sentiment information. There are many ABSA tasks, and the current dominant paradigm is to train tas…
Dense-ATOMIC: Towards Densely-connected ATOMIC with High Knowledge Coverage and Massive Multi-hop Paths
Xiangqing Shen, Siwei Wu, Rui Xia
ATOMIC is a large-scale commonsense knowledge graph (CSKG) containing everyday if-then knowledge triplets, i.e., {head event, relation, tail event}. The one-hop annotation manner m…
Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis
Yan Ling, Jianfei Yu, Rui Xia
As an important task in sentiment analysis, Multimodal Aspect-Based Sentiment Analysis (MABSA) has attracted increasing attention in recent years. However, previous approaches eith…