LLMBox: A Comprehensive Library for Large Language Models

1 code implementation • 8 Jul 2024 • Tianyi Tang, Yiwen Hu, Bingqian Li, Wenyang Luo, Zijing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Yuanqian Zhao, Luran Ding, Yuhao Wang, Zican Dong, Chunxuan Xia, Junyi Li, Kun Zhou, Wayne Xin Zhao, Ji-Rong Wen

To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs.

Sequence-level Semantic Representation Fusion for Recommender Systems

1 code implementation • 28 Feb 2024 • Lanling Xu, Zhen Tian, Bingqian Li, Junjie Zhang, Jinpeng Wang, Mingchen Cai, Wayne Xin Zhao

The core idea of our approach is to conduct a sequence-level semantic fusion approach by better integrating global contexts.

Mixture-of-Experts Sequential Recommendation

Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

no code implementations • 10 Jan 2024 • Lanling Xu, Junjie Zhang, Bingqian Li, Jinpeng Wang, Sheng Chen, Wayne Xin Zhao, Ji-Rong Wen

As for the use of LLMs as recommenders, we analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results based on the classification of LLMs.

Prompt Engineering Recommendation Systems