8 citations · 12 across the 14 of their papers we have counts for
14 papers
From Single- to Cross-Document: Benchmarking Multi-Granularity Event Analysis of Large Language Models
Tao Wen, Shuai Shao, Pei Ke +7
Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of documents. While large languag…
DASH-KV: Accelerating Long-Context LLM Inference via Asymmetric KV Cache Hashing
Jinyu Guo, Zhihan Zhang, Jiehui Xie +7
The quadratic computational complexity of the standard attention mechanism constitutes a fundamental bottleneck for large language models in long-context inference. While existing…
CARD: Non-Uniform Quantization of Visual Semantic Unit for Generative Recommendation
Yibiao Wei, Jie Zou, Pengfei Zhang +4
Generative recommendation frameworks typically represent items as discrete Semantic IDs (SIDs). While existing studies have sought to enhance SID construction by incorporating mult…
Lightweight LLM Agent Memory with Small Language Models
Jiaquan Zhang, Chaoning Zhang, Shuxu Chen +9
Although LLM agents can leverage tools for complex tasks, they still need memory to maintain cross-turn consistency and accumulate reusable information in long-horizon interactions…
Beyond Static: Related Questions Retrieval Through Conversations in Community Question Answering
Xiao Ao, Jie Zou, Yibiao Wei +2
In community question answering (cQA) platforms like Stack Overflow, related question retrieval is recognized as a fundamental task that allows users to retrieve related questions…
ALTER: Asymmetric LoRA for Token-Entropy-Guided Unlearning of LLMs
Xunlei Chen, Jinyu Guo, Yuang Li +5
Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a LLMs should not know is important for ensuring alignment…
Unleashing the Potential of Neighbors: Diffusion-based Latent Neighbor Generation for Session-based Recommendation
Yuhan Yang, Jie Zou, Guojia An +3
Session-based recommendation aims to predict the next item that anonymous users may be interested in, based on their current session interactions. Recent studies have demonstrated…
Multi-refined Feature Enhanced Sentiment Analysis Using Contextual Instruction
Peter Atandoh, Jie Zou, Weikang Guo +2
Sentiment analysis using deep learning and pre-trained language models (PLMs) has gained significant traction due to their ability to capture rich contextual representations. Howev…
MSCRS: Multi-modal Semantic Graph Prompt Learning Framework for Conversational Recommender Systems
Yibiao Wei, Jie Zou, Weikang Guo +3
Conversational Recommender Systems (CRSs) aim to provide personalized recommendations by interacting with users through conversations. Most existing studies of CRS focus on extract…
Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation
Guojia An, Jie Zou, Jiwei Wei +3
Conversational recommender systems aim to provide personalized recommendations by analyzing and utilizing contextual information related to dialogue. However, existing methods typi…
Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts
Jie Zou, Cheng Lin, Weikang Guo +4
Conversational recommender systems enable natural language conversations and thus lead to a more engaging and effective recommendation scenario. As the conversations for recommende…
PSCon: Product Search Through Conversations
Jie Zou, Mohammad Aliannejadi, Evangelos Kanoulas +5
Conversational Product Search ( CPS ) systems interact with users via natural language to offer personalized and context-aware product lists. However, most existing research on CPS…
NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models
Cheng Lin, Lujun Li, Dezhi Li +3
In this paper, we introduce Nested Low-Rank Adaptation (NoRA), a novel approach to parameter-efficient fine-tuning that extends the capabilities of Low-Rank Adaptation (LoRA) techn…
Cross-modal Consistency Learning with Fine-grained Fusion Network for Multimodal Fake News Detection
Jun Li, Yi Bin, Jie Zou +2
Previous studies on multimodal fake news detection have observed the mismatch between text and images in the fake news and attempted to explore the consistency of multimodal news b…