Zobrazeno 1 - 10
of 19 765
pro vyhledávání: '"A McAuley"'
Autor:
Wang, Shijian, Song, Linxin, Zhang, Jieyu, Shimizu, Ryotaro, Luo, Ao, Yao, Li, Chen, Cunjian, McAuley, Julian, Wu, Hanqian
Current multimodal language models (MLMs) evaluation and training approaches overlook the influence of instruction format, presenting an elephant-in-the-room problem. Previous research deals with this problem by manually crafting instructions, failin
Externí odkaz:
http://arxiv.org/abs/2412.08307
Conversational recommendation systems (CRS) leverage contextual information from conversations to generate recommendations but often struggle due to a lack of collaborative filtering (CF) signals, which capture user-item interaction patterns essentia
Externí odkaz:
http://arxiv.org/abs/2412.06949
Autor:
Wu, Junda, Lyu, Hanjia, Xia, Yu, Zhang, Zhehao, Barrow, Joe, Kumar, Ishita, Mirtaheri, Mehrnoosh, Chen, Hongjie, Rossi, Ryan A., Dernoncourt, Franck, Yu, Tong, Zhang, Ruiyi, Gu, Jiuxiang, Ahmed, Nesreen K., Wang, Yu, Chen, Xiang, Deilamsalehy, Hanieh, Park, Namyong, Kim, Sungchul, Yang, Huanrui, Mitra, Subrata, Hu, Zhengmian, Lipka, Nedim, Nguyen, Dang, Zhao, Yue, Luo, Jiebo, McAuley, Julian
Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as text, images, and audio, to perform complex tasks with high accuracy. Thi
Externí odkaz:
http://arxiv.org/abs/2412.02142
Autor:
Yoon, Se-eun, Wei, Xiaokai, Jiang, Yexi, Pareek, Rachit, Ong, Frank, Gao, Kevin, McAuley, Julian, Gong, Michelle
In this paper, we present a systematic effort to design, evaluate, and implement a realistic conversational recommender system (CRS). The objective of our system is to allow users to input free-form text to request recommendations, and then receive a
Externí odkaz:
http://arxiv.org/abs/2411.19352
While sequential recommendation achieves significant progress on capturing user-item transition patterns, transferring such large-scale recommender systems remains challenging due to the disjoint user and item groups across domains. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2411.01785
Autor:
Wu, Junda, Li, Xintong, Wang, Ruoyu, Xia, Yu, Xiong, Yuxin, Wang, Jianing, Yu, Tong, Chen, Xiang, Kveton, Branislav, Yao, Lina, Shang, Jingbo, McAuley, Julian
Offline evaluation of LLMs is crucial in understanding their capacities, though current methods remain underexplored in existing research. In this work, we focus on the offline evaluation of the chain-of-thought capabilities and show how to optimize
Externí odkaz:
http://arxiv.org/abs/2410.23703
Large language models (LLMs) have been used to generate query expansions augmenting original queries for improving information search. Recent studies also explore providing LLMs with initial retrieval results to generate query expansions more grounde
Externí odkaz:
http://arxiv.org/abs/2410.13765
Autor:
Shimizu, Ryotaro, Wada, Takashi, Wang, Yu, Kruse, Johannes, O'Brien, Sean, HtaungKham, Sai, Song, Linxin, Yoshikawa, Yuya, Saito, Yuki, Tsung, Fugee, Goto, Masayuki, McAuley, Julian
Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between the predicted and ground-truth explanations. However, this approach fai
Externí odkaz:
http://arxiv.org/abs/2410.13248
Autor:
Xu, Weihan, Liang, Paul Pu, Kim, Haven, McAuley, Julian, Berg-Kirkpatrick, Taylor, Dong, Hao-Wen
Teasers are an effective tool for promoting content in entertainment, commercial and educational fields. However, creating an effective teaser for long videos is challenging for it requires long-range multimodal modeling on the input videos, while ne
Externí odkaz:
http://arxiv.org/abs/2410.05586
Autor:
Novack, Zachary, Zhu, Ge, Casebeer, Jonah, McAuley, Julian, Berg-Kirkpatrick, Taylor, Bryan, Nicholas J.
Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration for score-based diffusion transformers via reducing both sampling ste
Externí odkaz:
http://arxiv.org/abs/2410.05167