PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs

Sentence-bert: Sentence embeddings using siamese bert-networks

Original

Nils Reimers and Iryna Gurevych. 2019 · 1908

Earlier work this paper cites.

Fine-tuning language models from human preferences

Original

Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019 · 1909

Earlier work this paper cites.

Language models are few-shot learners

Original

Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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 · 2005

Earlier work this paper cites.

Tweeteval: Unified benchmark and comparative evaluation for tweet classification

Original

Francesco Barbieri, Jose Camacho-Collados, Leonardo Neves, and Luis Espinosa-Anke. 2020 · 2010

Earlier work this paper cites.

Distilling the knowledge in a neural network

Original

Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015

Earlier work this paper cites.

Dialogue graph modeling for conversational machine reading

Siru Ouyang, Zhuosheng Zhang, and Hai Zhao. 2021 · 2021

Earlier work this paper cites.

Do as i can, not as i say: Grounding language in robotic affordances

Original

Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al. 2022 · 2022

Earlier work this paper cites.

In-context learning distillation: Transferring few-shot learning ability of pre-trained language models

Original

Yukun Huang, Yanda Chen, Zhou Yu, and Kathleen McKeown. 2022 · 2022

Earlier work this paper cites.

What makes good in-context examples for gpt-3?

Original

Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022 · 2022

Earlier work this paper cites.

Scienceworld: Is your agent smarter than a 5th grader?

Original

Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, and Prithviraj Ammanabrolu. 2022 · 2022

Earlier work this paper cites.

Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022

Earlier work this paper cites.

React: Synergizing reasoning and acting in language models

Original

Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022 · 2022

Earlier work this paper cites.

Rest meets react: Self-improvement for multi-step reasoning llm agent

Original

Renat Aksitov, Sobhan Miryoosefi, Zonglin Li, Daliang Li, Sheila Babayan, Kavya Kopparapu, Zachary Fisher, Ruiqi Guo, Sushant Prakash, Pranesh Srinivasan, et al. 2023 · 2023

Earlier work this paper cites.

Weak-to-strong generalization: Eliciting strong capabilities with weak supervision

Original

Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, et al. 2023 · 2023

Cited alongside, same era.

Retrieval-augmented generation for large language models: A survey

Original

Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023 · 2023

Cited alongside, same era.

Gemini: a family of highly capable multimodal models

Original

GeminiTeam, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023

Cited alongside, same era.

Knowledge distillation of large language models

Original

Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. 2023 · 2023

Cited alongside, same era.

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, et al. 2023 · 2023

Later among the works it cites.

Small language models improve giants by rewriting their outputs

Original

Giorgos Vernikos, Arthur Bražinskas, Jakub Adamek, Jonathan Mallinson, Aliaksei Severyn, and Eric Malmi. 2023 · 2023

Later among the works it cites.

Learning from mistakes via cooperative study assistant for large language models

Original

Danqing Wang and Lei Li. 2023 · 2023

Later among the works it cites.

Small models are valuable plug-ins for large language models

Original

Canwen Xu, Yichong Xu, Shuohang Wang, Yang Liu, Chenguang Zhu, and Julian McAuley. 2023 · 2023

Later among the works it cites.