Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Dai, Sunhao"'
Text embeddings enable various applications, but their performance deteriorates on longer texts. In this paper, we find that the performance degradation is due to a phenomenon called Length Collapse, where longer text embeddings collapse into a narro
Externí odkaz:
http://arxiv.org/abs/2410.24200
Autor:
Qu, Changle, Dai, Sunhao, Wei, Xiaochi, Cai, Hengyi, Wang, Shuaiqiang, Yin, Dawei, Xu, Jun, Wen, Ji-Rong
Tool learning enables Large Language Models (LLMs) to interact with external environments by invoking tools, serving as an effective strategy to mitigate the limitations inherent in their pre-training data. In this process, tool documentation plays a
Externí odkaz:
http://arxiv.org/abs/2410.08197
Reciprocal recommender systems~(RRS), conducting bilateral recommendations between two involved parties, have gained increasing attention for enhancing matching efficiency. However, the majority of existing methods in the literature still reuse conve
Externí odkaz:
http://arxiv.org/abs/2408.09748
Autor:
Tang, Jiakai, Dai, Sunhao, Sun, Zexu, Chen, Xu, Xu, Jun, Yu, Wenhui, Hu, Lantao, Jiang, Peng, Li, Han
In recent years, graph contrastive learning (GCL) has received increasing attention in recommender systems due to its effectiveness in reducing bias caused by data sparsity. However, most existing GCL models rely on heuristic approaches and usually a
Externí odkaz:
http://arxiv.org/abs/2407.10184
Recently, researchers have uncovered that neural retrieval models prefer AI-generated content (AIGC), called source bias. Compared to active search behavior, recommendation represents another important means of information acquisition, where users ar
Externí odkaz:
http://arxiv.org/abs/2405.17998
Autor:
Qu, Changle, Dai, Sunhao, Wei, Xiaochi, Cai, Hengyi, Wang, Shuaiqiang, Yin, Dawei, Xu, Jun, Wen, Ji-Rong
Recently, tool learning with large language models (LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems. Despite growing attention and rapid advancements in this field, the existing lite
Externí odkaz:
http://arxiv.org/abs/2405.17935
In real-world recommender systems, such as in the music domain, repeat consumption is a common phenomenon where users frequently listen to a small set of preferred songs or artists repeatedly. The key point of modeling repeat consumption is capturing
Externí odkaz:
http://arxiv.org/abs/2405.16550
Autor:
Dai, Sunhao, Liu, Weihao, Zhou, Yuqi, Pang, Liang, Ruan, Rongju, Wang, Gang, Dong, Zhenhua, Xu, Jun, Wen, Ji-Rong
The proliferation of Large Language Models (LLMs) has led to an influx of AI-generated content (AIGC) on the internet, transforming the corpus of Information Retrieval (IR) systems from solely human-written to a coexistence with LLM-generated content
Externí odkaz:
http://arxiv.org/abs/2405.16546
Autor:
Qu, Changle, Dai, Sunhao, Wei, Xiaochi, Cai, Hengyi, Wang, Shuaiqiang, Yin, Dawei, Xu, Jun, Wen, Ji-Rong
Recently, integrating external tools with Large Language Models (LLMs) has gained significant attention as an effective strategy to mitigate the limitations inherent in their pre-training data. However, real-world systems often incorporate a wide arr
Externí odkaz:
http://arxiv.org/abs/2405.16089
With the rapid advancements of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift. This evolution, while heralding new opportunities, introduce
Externí odkaz:
http://arxiv.org/abs/2404.11457