Instruction Fine-Tuning: Does Prompt Loss Matter?

Winogrande: An adversarial winograd schema challenge at scale

Original

Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 1907

Earlier work this paper cites.

Findings of the 2014 workshop on statistical machine translation

Ondřej Bojar, Christian Buck, Christian Federmann, Barry Haddow, Philipp Koehn, Johannes Leveling, Christof Monz, Pavel Pecina, Matt Post, Herve Saint-Amand, Radu Soricut, Lucia Specia, and Aleš Tamchyna. 2014 · 2014

Earlier work this paper cites.

Findings of the 2016 conference on machine translation

Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, and Marcos Zampieri. 2016 · 2016

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.

Piqa: Reasoning about physical commonsense in natural language

Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2020 · 2020

Earlier work this paper cites.

Learning to summarize with human feedback

Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020 · 2020

Earlier work this paper cites.

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

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Perric Cistac, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020

Earlier work this paper cites.

RL with KL penalties is better viewed as Bayesian inference

Tomasz Korbak, Ethan Perez, and Christopher Buckley. 2022 · 2022

Earlier work this paper cites.

TruthfulQA: Measuring how models mimic human falsehoods

Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022

Cited alongside, same era.

Model card and evaluations for claude models

Anthropic. 2023 · 2023

Cited alongside, same era.

Free dolly: Introducing the world’s first truly open instruction-tuned llm

Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023 · 2023

Cited alongside, same era.

Ultrafeedback: Boosting language models with high-quality feedback

Original

Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun. 2023 · 2023

Cited alongside, same era.

Establishing performance baselines in fine-tuning, retrieval-augmented generation and soft-prompting for non-specialist llm users

Original

Jennifer Dodgson, Lin Nanzheng, Julian Peh, Akira Rafhael Janson Pattirane, Alfath Daryl Alhajir, Eko Ridho Dinarto, Joseph Lim, and Syed Danyal Ahmad. 2023 · 2023

Instruction tuning with gpt-4

Original

Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. 2023 · 2023

Later among the works it cites.

AlpacaDataCleaned

Gene Ruebsamen. 2023 · 2023

Later among the works it cites.

Stanford alpaca: An instruction-following llama model

Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023

Later among the works it cites.

Rethinking privacy in machine learning pipelines from an information flow control perspective

Original

Lukas Wutschitz, Boris Köpf, Andrew Paverd, Saravan Rajmohan, Ahmed Salem, Shruti Tople, Santiago Zanella-Béguelin, Menglin Xia, and Victor Rühle. 2023 · 2023

Later among the works it cites.

Llama 3 model card

AI@Meta. 2024 · 2024

Closest in time.