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20172026
most citedExplainable Outfit Recommendation with Joint Outfit Matching and Comment Generation

128 citations · 927 across the 99 of their papers we have counts for

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Showing 2024Show all

22 papers · 1 filter

cs.CL2024

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…

cs.CL2024

Uncovering Overfitting in Large Language Model Editing

Mengqi Zhang, Xiaotian Ye, Qiang Liu +3

Knowledge editing has been proposed as an effective method for updating and correcting the internal knowledge of Large Language Models (LLMs). However, existing editing methods oft…

cs.CL2024

MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization

Yougang Lyu, Lingyong Yan, Zihan Wang +4

As large language models (LLMs) are rapidly advancing and achieving near-human capabilities on specific tasks, aligning them with human values is becoming more urgent. In scenarios…

cs.IR2024★ 21 cited

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…

cs.CL2024

Enhancing Multi-hop Reasoning through Knowledge Erasure in Large Language Model Editing

Mengqi Zhang, Bowen Fang, Qiang Liu +4

Large language models (LLMs) face challenges with internal knowledge inaccuracies and outdated information. Knowledge editing has emerged as a pivotal approach to mitigate these is…

cs.MA2024

Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning

Zhiwei Xu, Hangyu Mao, Nianmin Zhang +8

In partially observable multi-agent systems, agents typically only have access to local observations. This severely hinders their ability to make precise decisions, particularly du…