Search Results for author: Luo Ji

Found 14 papers, 4 papers with code

Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation

no code implementations6 Dec 2024 Xiaoyu Wang, Ningyuan Xi, Teng Chen, Qingqing Gu, Yue Zhao, Xiaokai Chen, Zhonglin Jiang, Yong Chen, Luo Ji

Large Language Models (LLM) are usually fine-tuned to participate in dyadic or two-party dialogues, which can not adapt well to multi-party dialogues (MPD), which hinders their applications in such scenarios including multi-personal meetings, discussions and daily communication.

Dialogue Generation

Online Decision MetaMorphFormer: A Casual Transformer-Based Reinforcement Learning Framework of Universal Embodied Intelligence

no code implementations11 Sep 2024 Luo Ji, Runji Lin

Interactive artificial intelligence in the motion control field is an interesting topic, especially when universal knowledge is adaptive to multiple tasks and universal environments.

Reinforcement Learning (RL)

An Adaptive Framework of Geographical Group-Specific Network on O2O Recommendation

no code implementations28 Dec 2023 Luo Ji, Jiayu Mao, Hailong Shi, Qian Li, Yunfei Chu, Hongxia Yang

Online to offline recommendation strongly correlates with the user and service's spatiotemporal information, therefore calling for a higher degree of model personalization.

Intra-session Context-aware Feed Recommendation in Live Systems

no code implementations30 Sep 2022 Luo Ji, Gao Liu, Mingyang Yin, Hongxia Yang

Feed recommendation allows users to constantly browse items until feel uninterested and leave the session, which differs from traditional recommendation scenarios.

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

1 code implementation11 Nov 2021 Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, Anpeng Wu, Fengda Zhang, Ziqi Tan, Kun Kuang, Chao Wu, Fei Wu, Jingren Zhou, Hongxia Yang

However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.

Cloud Computing Edge-computing +1

Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation

no code implementations20 Aug 2021 Luo Ji, Qin Qi, Bingqing Han, Hongxia Yang

In RL-LTV, the critic studies historical trajectories of items and predict the future LTV of fresh item, while the actor suggests a score-based policy which maximizes the future LTV expectation.

Recommendation Systems reinforcement-learning +2

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