Zobrazeno 1 - 10
of 743
pro vyhledávání: '"Zhang, Weinan"'
Publikováno v:
ACM Transactions on Information Systems 40(1): 9:1-9:44 (2022)
Incorporating external knowledge into dialogue generation has been proven to benefit the performance of an open-domain Dialogue System (DS), such as generating informative or stylized responses, controlling conversation topics. In this article, we st
Externí odkaz:
http://arxiv.org/abs/2411.09166
Argumentative essay generation (AEG) aims to generate complete texts on specific controversial topics or debates. Although current AEG methods can generate individual opinions, they often overlook the high-level connections between these opinions. Th
Externí odkaz:
http://arxiv.org/abs/2410.22642
Autor:
Weng, Muyan, Xi, Yunjia, Liu, Weiwen, Chen, Bo, Lin, Jianghao, Tang, Ruiming, Zhang, Weinan, Yu, Yong
As the last stage of recommender systems, re-ranking generates a re-ordered list that aligns with the user's preference. However, previous works generally focus on item-level positive feedback as history (e.g., only clicked items) and ignore that use
Externí odkaz:
http://arxiv.org/abs/2410.20778
Making use of off-the-shelf resources of resource-rich languages to transfer knowledge for low-resource languages raises much attention recently. The requirements of enabling the model to reach the reliable performance lack well guided, such as the s
Externí odkaz:
http://arxiv.org/abs/2410.18430
Autor:
Zhang, Kangning, Jin, Jiarui, Qin, Yingjie, Su, Ruilong, Lin, Jianghao, Yu, Yong, Zhang, Weinan
Current multimodal recommendation models have extensively explored the effective utilization of multimodal information; however, their reliance on ID embeddings remains a performance bottleneck. Even with the assistance of multimodal information, opt
Externí odkaz:
http://arxiv.org/abs/2410.19276
Recommender systems (RS) are pivotal in managing information overload in modern digital services. A key challenge in RS is efficiently processing vast item pools to deliver highly personalized recommendations under strict latency constraints. Multi-s
Externí odkaz:
http://arxiv.org/abs/2410.16080
Crafting effective features is a crucial yet labor-intensive and domain-specific task within machine learning pipelines. Fortunately, recent advancements in Large Language Models (LLMs) have shown promise in automating various data science tasks, inc
Externí odkaz:
http://arxiv.org/abs/2410.12865
What will information entry look like in the next generation of digital products? Since the 1970s, user access to relevant information has relied on domain-specific architectures of information retrieval (IR). Over the past two decades, the advent of
Externí odkaz:
http://arxiv.org/abs/2410.09713
Autor:
Wang, Jun, Fang, Meng, Wan, Ziyu, Wen, Muning, Zhu, Jiachen, Liu, Anjie, Gong, Ziqin, Song, Yan, Chen, Lei, Ni, Lionel M., Yang, Linyi, Wen, Ying, Zhang, Weinan
In this technical report, we introduce OpenR, an open-source framework designed to integrate key components for enhancing the reasoning capabilities of large language models (LLMs). OpenR unifies data acquisition, reinforcement learning training (bot
Externí odkaz:
http://arxiv.org/abs/2410.09671
Autor:
Lin, Qiqiang, Wen, Muning, Peng, Qiuying, Nie, Guanyu, Liao, Junwei, Wang, Jun, Mo, Xiaoyun, Zhou, Jiamu, Cheng, Cheng, Zhao, Yin, Zhang, Weinan
Large language models have demonstrated impressive value in performing as autonomous agents when equipped with external tools and API calls. Nonetheless, effectively harnessing their potential for executing complex tasks crucially relies on enhanceme
Externí odkaz:
http://arxiv.org/abs/2410.04587