The tensorflow's intro from daod

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Computation using data flow graphs for scalable machine learning

License: Apache License 2.0

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tensorflow's Introduction

👋 About me

I am currently a postdoctoral researcher at the Gaoling School of Artificial Intelligence, Renmin University of China. I earned my PhD from University of Montreal, where I was mentored by Prof. Jian-Yun Nie.
I completed my master's (2019) and bachelor's (2016) degrees at Renmin University of China, under the guidance of Prof. Zhicheng Dou and Prof. Ji-Rong Wen, delving into various NLP challenges.
Research interests: Retrieval-augmented generation, large language models for information retrieval, session-based document ranking

📎 Homepages

Personal page: https://daod.github.io/
Google scholar: https://scholar.google.com/citations?user=tBqVOWsAAAAJ
DBLP: https://dblp.org/pid/71/9704-1.html

🔥 News

2024.5: We publish a new toolkit ⚡FlashRAG, which can help implement RAG methods quickly! See more details.
2024.5: Congrats! Our three papers have been accepted by ACL 2024!
2024.4: We write a new survey about generative information retrieval. See more details.
2024.1: We propose a new instruction tuning dataset (INTERS) for unlocking the power of LLMs on search tasks. See more details.
2023.11: We analyze the risk of data leakage in LLM pre-training and write a new paper to alert this problem. See more details.
2023.8: We write a new survey about applying large language models for information retrieval. See more details.
2023.8: We publish a new version of YuLan-Chat. It achieves better performance than the official LLaMA-2 and LLaMA-2-Chat on MMLU, C-Eval, and AGI-Gaokao benchmarks!

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