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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research

Published in arXiv, 2024

FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 32 pre-processed benchmark RAG datasets and 13 state-of-the-art RAG algorithms.

Recommended citation: Guanting Dong, Yutao Zhu, Chenghao Zhang, Zechen Wang, Zhicheng Dou, Ji-Rong Wen: Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation. CoRR abs/2406.18676 (2024) http://snownation101.github.io/files/FlashRAG.pdf

Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation

Published in arXiv, 2024

DPA-RAG is a universal framework for aligning diverse preference knowledge within RAG systems, consisting of three main components: Preference Knowledge Construction, Reranker-LLM Alignment and LLM Self-Alignment.

Recommended citation: Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zhicheng Dou, Zheng Liu, Ji-Rong Wen: INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning. CoRR abs/2401.06532 (2024) http://snownation101.github.io/files/DPA.pdf

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.