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pro vyhledávání: '"Li, ChenLiang"'
While fusing the capacities and advantages of various large language models (LLMs) offers a pathway to construct more powerful and versatile models, a fundamental challenge is to properly select advantageous model during the training. Existing fusion
Externí odkaz:
http://arxiv.org/abs/2408.04998
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks. However, their `large' scale introduces significant computational and storage challenges, particularly in managing the
Externí odkaz:
http://arxiv.org/abs/2407.02328
Autor:
Li, Chenliang, Zeng, Siliang, Liao, Zeyi, Li, Jiaxiang, Kang, Dongyeop, Garcia, Alfredo, Hong, Mingyi
Aligning human preference and value is an important requirement for building contemporary foundation models and embodied AI. However, popular approaches such as reinforcement learning with human feedback (RLHF) break down the task into successive sta
Externí odkaz:
http://arxiv.org/abs/2406.06874
Aligning human preference and value is an important requirement for contemporary foundation models. State-of-the-art techniques such as Reinforcement Learning from Human Feedback (RLHF) often consist of two stages: 1) supervised fine-tuning (SFT), wh
Externí odkaz:
http://arxiv.org/abs/2405.17888
Autor:
Chen, Hongzhan, Chen, Hehong, Yan, Ming, Xu, Wenshen, Gao, Xing, Shen, Weizhou, Quan, Xiaojun, Li, Chenliang, Zhang, Ji, Huang, Fei, Zhou, Jingren
Large language models (LLMs) have advanced the development of various AI conversational agents, including role-playing conversational agents that mimic diverse characters and human behaviors. While prior research has predominantly focused on enhancin
Externí odkaz:
http://arxiv.org/abs/2403.13679
Autor:
Liu, Haowei, Shi, Yaya, Xu, Haiyang, Yuan, Chunfeng, Ye, Qinghao, Li, Chenliang, Yan, Ming, Zhang, Ji, Huang, Fei, Li, Bing, Hu, Weiming
In vision-language pre-training (VLP), masked image modeling (MIM) has recently been introduced for fine-grained cross-modal alignment. However, in most existing methods, the reconstruction targets for MIM lack high-level semantics, and text is not s
Externí odkaz:
http://arxiv.org/abs/2403.00249
The session-based recommendation (SBR) garners increasing attention due to its ability to predict anonymous user intents within limited interactions. Emerging efforts incorporate various kinds of side information into their methods for enhancing task
Externí odkaz:
http://arxiv.org/abs/2402.17129
Autor:
Liu, Haowei, Shi, Yaya, Xu, Haiyang, Yuan, Chunfeng, Ye, Qinghao, Li, Chenliang, Yan, Ming, Zhang, Ji, Huang, Fei, Li, Bing, Hu, Weiming
In video-text retrieval, most existing methods adopt the dual-encoder architecture for fast retrieval, which employs two individual encoders to extract global latent representations for videos and texts. However, they face challenges in capturing fin
Externí odkaz:
http://arxiv.org/abs/2402.16769
Autor:
Shen, Weizhou, Li, Chenliang, Chen, Hongzhan, Yan, Ming, Quan, Xiaojun, Chen, Hehong, Zhang, Ji, Huang, Fei
Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs, empowering them to interact with external tools (e.g., APIs, functions) and complete various tasks in a self-directed fashion. The challenge of tool use deman
Externí odkaz:
http://arxiv.org/abs/2401.07324
Given the exceptional performance of proprietary large language models (LLMs) like GPT-4, recent research has increasingly focused on boosting the capabilities of smaller models through knowledge distillation (KD) from these powerful yet black-box te
Externí odkaz:
http://arxiv.org/abs/2401.07013