"Seeing the Big through the Small": Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Original

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907

Earlier work this paper cites.

Stop measuring calibration when humans disagree

Joris Baan, Wilker Aziz, Barbara Plank, and Raquel Fernandez. 2022 · 1915

Earlier work this paper cites.

On information and sufficiency

Solomon Kullback and Richard A Leibler. 1951 · 1951

Earlier work this paper cites.

Combinatorial methods in density estimation

Luc Devroye and Gábor Lugosi. 2001 · 2001

Earlier work this paper cites.

A new metric for probability distributions

Dominik Maria Endres and Johannes E. Schindelin. 2003 · 2003

Earlier work this paper cites.

The PASCAL recognising textual entailment challenge

Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005

Earlier work this paper cites.

Local textual inference : It’s hard to circumscribe , but you know it when you see it - and nlp needs it

Christopher D. Manning. 2006 · 2006

Earlier work this paper cites.

Measuring and testing dependence by correlation of distances

Gábor J. Székely, Maria L. Rizzo, and Nail K. Bakirov. 2007 · 2007

Earlier work this paper cites.

Learning part-of-speech taggers with inter-annotator agreement loss

Barbara Plank, Dirk Hovy, and Anders Søgaard. 2014 · 2014

Earlier work this paper cites.

Truth is a lie: Crowd truth and the seven myths of human annotation

Lora Aroyo and Chris Welty. 2015 · 2015

Earlier work this paper cites.

A large annotated corpus for learning natural language inference

Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015

Earlier work this paper cites.

XNLI: Evaluating cross-lingual sentence representations

Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, and Veselin Stoyanov. 2018 · 2018

Earlier work this paper cites.

A broad-coverage challenge corpus for sentence understanding through inference

Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018 · 2018

Earlier work this paper cites.

BERT: pre-training of deep bidirectional transformers for language understanding

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019

Earlier work this paper cites.

When does label smoothing help?

Rafael Müller, Simon Kornblith, and Geoffrey E. Hinton. 2019 · 2019

Earlier work this paper cites.

Inherent disagreements in human textual inferences

Ellie Pavlick and Tom Kwiatkowski. 2019 · 2019

Earlier work this paper cites.

GLUE: A multi-task benchmark and analysis platform for natural language understanding

Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019

Earlier work this paper cites.

What can we learn from collective human opinions on natural language inference data?

Yixin Nie, Xiang Zhou, and Mohit Bansal. 2020 · 2020

Earlier work this paper cites.

Beyond black & white: Leveraging annotator disagreement via soft-label multi-task learning

Tommaso Fornaciari, Alexandra Uma, Silviu Paun, Barbara Plank, Dirk Hovy, and Massimo Poesio. 2021a · 2021

Cited alongside, same era.

Beyond black & white: Leveraging annotator disagreement via soft-label multi-task learning

Tommaso Fornaciari, Alexandra Uma, Silviu Paun, Barbara Plank, Dirk Hovy, and Massimo Poesio. 2021b · 2021

Cited alongside, same era.

Learning from disagreement: A survey

Alexandra Uma, Tommaso Fornaciari, Dirk Hovy, Silviu Paun, Barbara Plank, and Massimo Poesio. 2021 · 2021

Cited alongside, same era.

Dealing with disagreements: Looking beyond the majority vote in subjective annotations

Aida Mostafazadeh Davani, Mark Díaz, and Vinodkumar Prabhakaran. 2022 · 2022

Cited alongside, same era.

Can language models learn from explanations in context?

Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory W. Mathewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane Wang, and Felix Hill. 2022 · 2022

Cited alongside, same era.

Do LLMs exhibit human-like response biases? A case study in survey design

Original

Lindia Tjuatja, Valerie Chen, Sherry Tongshuang Wu, Ameet Talwalkar, and Graham Neubig. 2023 · 2023

Later among the works it cites.

Llama 2: Open foundation and fine-tuned chat models

Original

Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023

Later among the works it cites.

Using Natural Language Explanations to Rescale Human Judgments

Original

Manya Wadhwa, Jifan Chen, Junyi Jessy Li, and Greg Durrett. 2023 · 2023

Later among the works it cites.