AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation

Language models are few-shot learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901

Earlier work this paper cites.

Asymptotically efficient adaptive allocation rules

Tze Leung Lai and Herbert Robbins. 1985 · 1985

Earlier work this paper cites.

Gradient-based learning applied to document recognition

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. 1998 · 1998

Earlier work this paper cites.

Using confidence bounds for exploitation-exploration trade-offs

Peter Auer. 2002 · 2002

Earlier work this paper cites.

Finite-time analysis of the multiarmed bandit problem

Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002 · 2002

Earlier work this paper cites.

Multi-armed bandit algorithms and empirical evaluation

Joannes Vermorel and Mehryar Mohri. 2005 · 2005

Earlier work this paper cites.

A contextual-bandit approach to personalized news article recommendation

Lihong Li, Wei Chu, John Langford, and Robert E Schapire. 2010 · 2010

Earlier work this paper cites.

An empirical evaluation of thompson sampling

Olivier Chapelle and Lihong Li. 2011 · 2011

Earlier work this paper cites.

Thompson sampling for contextual bandits with linear payoffs

Shipra Agrawal and Navin Goyal. 2013 · 2013

Earlier work this paper cites.

Efficient and robust automated machine learning

Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. 2015 · 2015

Earlier work this paper cites.

Evaluation of a tree-based pipeline optimization tool for automating data science

Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore. 2016 · 2016

Earlier work this paper cites.

Automated machine learning: Methods, systems, challenges

Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren. 2019 · 2019

Earlier work this paper cites.

Natural Questions: A Benchmark for Question Answering Research

Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019

Cited alongside, same era.

Flaml: A fast and lightweight automl library

Chi Wang, Qingyun Wu, Markus Weimer, and Erkang Zhu. 2019 · 2019

Cited alongside, same era.

Retrieval-augmented generation for knowledge-intensive nlp tasks

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020

Cited alongside, same era.

Mpnet: Masked and permuted pre-training for language understanding

Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2020 · 2020

Cited alongside, same era.

Unsupervised dense information retrieval with contrastive learning

Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021 · 2021

Cited alongside, same era.

Enabling large language models to generate text with citations

Tianyu Gao, Howard Yen, Jiatong Yu, and Danqi Chen. 2023 · 2023

Later among the works it cites.

Autokeras: An automl library for deep learning

Haifeng Jin, François Chollet, Qingquan Song, and Xia Hu. 2023 · 2023

Later among the works it cites.

Query rewriting for retrieval-augmented large language models

Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023 · 2023

Later among the works it cites.

Gpt-4 technical report

Original

OpenAI. 2023 · 2023

Later among the works it cites.

Automatic prompt optimization with “gradient descent” and beam search

Reid Pryzant, Dan Iter, Jerry Li, Yin Lee, Chenguang Zhu, and Michael Zeng. 2023 · 2023

Later among the works it cites.

Query2doc: Query expansion with large language models