InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval

MS MARCO: A human generated machine reading comprehension dataset

Original

T. Nguyen, M. Rosenberg, X. Song, J. Gao, S. Tiwary, R. Majumder, and L. Deng · 2016

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Mesh-TensorFlow: Deep learning for supercomputers

N. Shazeer, Y. Cheng, N. Parmar, D. Tran, A. Vaswani, P. Koanantakool, P. Hawkins, H. Lee, M. Hong, C. Young, R. Sepassi, and B. Hechtman · 2018

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Natural questions: A benchmark for question answering research

T. Kwiatkowski, J. Palomaki, O. Redfield, M. Collins, A. Parikh, C. Alberti, D. Epstein, I. Polosukhin, J. Devlin, K. Lee, K. Toutanova, L. Jones, M. Kelcey, M.-W. Chang, A. M. Dai, J. Uszkoreit, Q. Le, and S. Petrov · 2019

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PyTorch: An Imperative Style, High-Performance Deep Learning Library

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019

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

Original

C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2019

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Document ranking with a pretrained sequence-to-sequence model

R. Nogueira, Z. Jiang, R. Pradeep, and J. Lin · 2020

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Transformers: State-of-the-Art Natural Language Processing

T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, C. Ma, Y. Jernite, J. Plu, C. Xu, T. Le Scao, S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush · 2020

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The pile: An 800gb dataset of diverse text for language modeling

Original

L. Gao, S. Biderman, S. Black, L. Golding, T. Hoppe, C. Foster, J. Phang, H. He, A. Thite, N. Nabeshima, S. Presser, and C. Leahy · 2021

Cited alongside, same era.

Pyserini: A Python toolkit for reproducible information retrieval research with sparse and dense representations

J. Lin, X. Ma, S.-C. Lin, J.-H. Yang, R. Pradeep, and R. Nogueira · 2021

Cited alongside, same era.

Simplified data wrangling with ir_datasets

S. MacAvaney, A. Yates, S. Feldman, D. Downey, A. Cohan, and N. Goharian · 2021

Cited alongside, same era.

Large dual encoders are generalizable retrievers

Original

J. Ni, C. Qu, J. Lu, Z. Dai, G. H. Ábrego, J. Ma, V. Y. Zhao, Y. Luan, K. B. Hall, M. Chang, and Y. Yang · 2021

Cited alongside, same era.

BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models

N. Thakur, N. Reimers, A. Rücklé, A. Srivastava, and I. Gurevych · 2021

Inpars: Unsupervised dataset generation for information retrieval

L. Bonifacio, H. Abonizio, M. Fadaee, and R. Nogueira · 2022

Later among the works it cites.

Promptagator: Few-shot dense retrieval from 8 examples, 2022

Z. Dai, V. Y. Zhao, J. Ma, Y. Luan, J. Ni, J. Lu, A. Bakalov, K. Guu, K. B. Hall, and M.-W. Chang · 2022

Later among the works it cites.

No parameter left behind: How distillation and model size affect zero-shot retrieval

Original

G. Rosa, L. Bonifacio, V. Jeronymo, H. Abonizio, M. Fadaee, R. Lotufo, and R. Nogueira · 2022

Later among the works it cites.

Finetuned language models are zero-shot learners

J. Wei, M. Bosma, V. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le · 2022

Later among the works it cites.

Inpars-light: Cost-effective unsupervised training of efficient rankers, 2023

L. Boytsov, P. Patel, V. Sourabh, R. Nisar, S. Kundu, R. Ramanathan, and E. Nyberg · 2023

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