SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains

Towards faithful and robust LLM specialists for evidence-based question-answering

Tobias Schimanski, Jingwei Ni, Mathias Kraus, Elliott Ash, and Markus Leippold. 2024 · 1931

Earlier work this paper cites.

Semi-supervised self-training of object detection models

Chuck Rosenberg, Martial Hebert, and Henry Schneiderman. 2005 · 2005

Earlier work this paper cites.

Semantic parsing on Freebase from question-answer pairs

Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013

Earlier work this paper cites.

An overview of the bioasq large-scale biomedical semantic indexing and question answering competition

George Tsatsaronis, Georgios Balikas, Prodromos Malakasiotis, Ioannis Partalas, Matthias Zschunke, Michael R Alvers, Dirk Weissenborn, Anastasia Krithara, Sergios Petridis, Dimitris Polychronopoulos, et al. 2015 · 2015

Earlier work this paper cites.

SQuAD: 100,000+ questions for machine comprehension of text

Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016

Earlier work this paper cites.

Overview of the medical question answering task at trec 2017 liveqa

Asma Ben Abacha, Eugene Agichtein, Yuval Pinter, and Dina Demner-Fushman. 2017 · 2017

Earlier work this paper cites.

Crowdsourcing multiple choice science questions

Johannes Welbl, Nelson F. Liu, and Matt Gardner. 2017 · 2017

Earlier work this paper cites.

Think you have solved question answering? try arc, the ai2 reasoning challenge

Original

Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018

Earlier work this paper cites.

The NarrativeQA reading comprehension challenge

Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018 · 2018

Earlier work this paper cites.

Can a suit of armor conduct electricity? a new dataset for open book question answering

Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018

Earlier work this paper cites.

FEVER: a large-scale dataset for fact extraction and VERification

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018

Earlier work this paper cites.

Bridging the gap between consumers medication questions and trusted answers

Asma Ben Abacha, Yassine Mrabet, Mark Sharp, Travis R Goodwin, Sonya E Shooshan, and Dina Demner-Fushman. 2019 · 2019

Earlier work this paper cites.

BoolQ: Exploring the surprising difficulty of natural yes/no questions

Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019 · 2019

Earlier work this paper cites.

Quoref: A reading comprehension dataset with questions requiring coreferential reasoning

Pradeep Dasigi, Nelson F. Liu, Ana Marasović, Noah A. Smith, and Matt Gardner. 2019 · 2019

Earlier work this paper cites.

DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs

Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019

Earlier work this paper cites.

ELI5: Long form question answering

Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019 · 2019

Earlier work this paper cites.

PubMedQA: A dataset for biomedical research question answering

Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 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

Earlier work this paper cites.

Reasoning over paragraph effects in situations

Kevin Lin, Oyvind Tafjord, Peter Clark, and Matt Gardner. 2019 · 2019

Earlier work this paper cites.

Decoupled weight decay regularization

Ilya Loshchilov and Frank Hutter. 2019 · 2019

Earlier work this paper cites.

Pseudo-labeling and confirmation bias in deep semi-supervised learning

Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness. 2020 · 2020

Earlier work this paper cites.

Retrieval-augmented generation for knowledge-intensive NLP tasks

Patrick S. H. 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

Earlier work this paper cites.

Logiqa: A challenge dataset for machine reading comprehension with logical reasoning

Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang. 2020 · 2020

Earlier work this paper cites.

The effect of natural distribution shift on question answering models

John Miller, Karl Krauth, Benjamin Recht, and Ludwig Schmidt. 2020 · 2020

Earlier work this paper cites.

Exploring the limits of transfer learning with a unified text-to-text transformer

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020

Earlier work this paper cites.

Improving question answering model robustness with synthetic adversarial data generation

Max Bartolo, Tristan Thrush, Robin Jia, Sebastian Riedel, Pontus Stenetorp, and Douwe Kiela. 2021 · 2021

Earlier work this paper cites.

Self-training improves pre-training for natural language understanding

Jingfei Du, Edouard Grave, Beliz Gunel, Vishrav Chaudhary, Onur Celebi, Michael Auli, Veselin Stoyanov, and Alexis Conneau. 2021 · 2021

Earlier work this paper cites.

Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies

Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021 · 2021

Cited alongside, same era.

Measuring massive multitask language understanding

Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021

Cited alongside, same era.

What disease does this patient have? a large-scale open domain question answering dataset from medical exams

Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2021 · 2021

Cited alongside, same era.

Retrieval augmentation reduces hallucination in conversation

Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021 · 2021

Cited alongside, same era.

STraTA: Self-training with task augmentation for better few-shot learning

Tu Vu, Minh-Thang Luong, Quoc Le, Grady Simon, and Mohit Iyyer. 2021 · 2021

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Chain-of-note: Enhancing robustness in retrieval-augmented language models

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Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, and Dong Yu. 2023 · 2023

Later among the works it cites.

Alpacare: Instruction-tuned large language models for medical application

Original

Xinlu Zhang, Chenxin Tian, Xianjun Yang, Lichang Chen, Zekun Li, and Linda Ruth Petzold. 2023 · 2023

Later among the works it cites.

Self-RAG: Learning to retrieve, generate, and critique through self-reflection

Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024 · 2024

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Instruction pre-training: Language models are supervised multitask learners

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Daixuan Cheng, Yuxian Gu, Shaohan Huang, Junyu Bi, Minlie Huang, and Furu Wei. 2024 · 2024

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Understand what llm needs: Dual preference alignment for retrieval-augmented generation