21 citations · 75 across the 32 of their papers we have counts for
37 papers
Optimizing VLP-aligned Multimodal Intent Representation with Correct Visual Instantiation for Zero-Shot Composed Image Retrieval
Xuri Ge, Chunhao Wang, Junchen Fu +7
ZS-CIR aims to retrieve a target image from a reference image and a modification text without paired supervision, typically by encoding composed queries as text-dominant representa…
Token-Level Credit Assignment Optimization for Generative Document Retrieval
Xinpeng Zhao, Yang Liu, Ran Chen +6
Generative retrieval models perform document retrieval by autoregressively generating document identifiers (DocIDs). This process naturally forms a sequential decision problem, i.e…
Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning
Zhao Yang, Yuxuan Jiang, Ting-Chih Chen +18
Reinforcement learning (RL) has become central to LLM post-training, yet the methods that dominate current pipelines, PPO and GRPO, represent only a narrow slice of what RL offers.…
SA-CAISR: Stage-Adaptive and Conflict-Aware Incremental Sequential Recommendation
Xiaomeng Song, Xinru Wang, Hanbing Wang +4
Sequential recommendation (SR) aims to predict a user's next action by learning from their historical interaction sequences. In real-world applications, these models require period…
Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Tasks
Wentao Deng, Jiahuan Pei, Zhiwei Xu +3
A multi-agent system (MAS) enhances its capacity to solve complex natural language processing (NLP) tasks through collaboration among multiple agents, where consensus-seeking serve…
OpenReward: Learning to Reward Long-form Agentic Tasks via Reinforcement Learning
Ziyou Hu, Zhengliang Shi, Minghang Zhu +5
Reward models (RMs) have become essential for aligning large language models (LLMs), serving as scalable proxies for human evaluation in both training and inference. However, exist…
Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfare
Zhengliang Shi, Ruotian Ma, Jen-tse Huang +14
Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare. However, the principles and values that guide these models when distri…
Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems
Minghang Zhu, Zhengliang Shi, Zhiwei Xu +5
The advancement of large language models (LLMs) has enabled the construction of multi-agent systems to solve complex tasks by dividing responsibilities among specialized agents, su…
Curriculum Approximate Unlearning for Session-based Recommendation
Liu Yang, Zhaochun Ren, Ziqi Zhao +7
Approximate unlearning for session-based recommendation refers to eliminating the influence of specific training samples from the recommender without retraining of (sub-)models. Gr…
Disentangling Knowledge Representations for Large Language Model Editing
Mengqi Zhang, Zisheng Zhou, Xiaotian Ye +4
Knowledge Editing has emerged as a promising solution for efficiently updating embedded knowledge in large language models (LLMs). While existing approaches demonstrate effectivene…
Replication and Exploration of Generative Retrieval over Dynamic Corpora
Zhen Zhang, Xinyu Ma, Weiwei Sun +6
Generative retrieval (GR) has emerged as a promising paradigm in information retrieval (IR). However, most existing GR models are developed and evaluated using a static document co…
Improving Sequential Recommenders through Counterfactual Augmentation of System Exposure
Ziqi Zhao, Zhaochun Ren, Jiyuan Yang +7
In sequential recommendation (SR), system exposure refers to items that are exposed to the user. Typically, only a few of the exposed items would be interacted with by the user. Al…
Constrained Auto-Regressive Decoding Constrains Generative Retrieval
Shiguang Wu, Zhaochun Ren, Xin Xin +5
Generative retrieval seeks to replace traditional search index data structures with a single large-scale neural network, offering the potential for improved efficiency and seamless…
Self-Adaptive Cognitive Debiasing for Large Language Models in Decision-Making
Yougang Lyu, Shijie Ren, Yue Feng +4
Large language models (LLMs) have shown potential in supporting decision-making applications, particularly as personal assistants in the financial, healthcare, and legal domains. W…
UIPE: Enhancing LLM Unlearning by Removing Knowledge Related to Forgetting Targets
Wenyu Wang, Mengqi Zhang, Xiaotian Ye +3
Large Language Models (LLMs) inevitably acquire harmful information during training on massive datasets. LLM unlearning aims to eliminate the influence of such harmful information…
Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models
Zhengliang Shi, Yuhan Wang, Lingyong Yan +4
Tool learning aims to augment large language models (LLMs) with diverse tools, enabling them to act as agents for solving practical tasks. Due to the limited context length of tool…
A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition
Zihan Wang, Ziqi Zhao, Yougang Lyu +3
Zero-shot named entity recognition (NER) aims to develop entity recognition systems from unannotated text corpora. This task presents substantial challenges due to minimal human in…
ASLoRA: Adaptive Sharing Low-Rank Adaptation Across Layers
Junyan Hu, Xue Xiao, Mengqi Zhang +4
As large language models (LLMs) grow in size, traditional full fine-tuning becomes increasingly impractical due to its high computational and storage costs. Although popular parame…
The 1st Workshop on Human-Centered Recommender Systems
Kaike Zhang, Yunfan Wu, Yougang lyu +6
Recommender systems are quintessential applications of human-computer interaction. Widely utilized in daily life, they offer significant convenience but also present numerous chall…
R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation
Zihan Wang, Xuri Ge, Joemon M. Jose +4
Retrieval-augmented generation (RAG) has gained wide attention as the key component to improve generative models with external knowledge augmentation from information retrieval. It…
Towards Empathetic Conversational Recommender Systems
Xiaoyu Zhang, Ruobing Xie, Yougang Lyu +7
Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained language m…
MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter
Jitai Hao, WeiWei Sun, Xin Xin +4
Parameter-Efficient Fine-tuning (PEFT) facilitates the fine-tuning of Large Language Models (LLMs) under limited resources. However, the fine-tuning performance with PEFT on comple…
Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering
Zhengliang Shi, Weiwei Sun, Shen Gao +3
Multi-Hop Question Answering (MHQA) tasks present a significant challenge for large language models (LLMs) due to the intensive knowledge required. Current solutions, like Retrieva…
ExcluIR: Exclusionary Neural Information Retrieval
Wenhao Zhang, Mengqi Zhang, Shiguang Wu +5
Exclusion is an important and universal linguistic skill that humans use to express what they do not want. However, in information retrieval community, there is little research on…