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Research into methods for improving the performance of large language models (LLMs) through fine-tuning, retrieval-augmented generation (RAG) and soft-prompting has tended to focus on the use of highly technical or high-cost techniques, making many of the newly discovered approaches comparatively inaccessible to non-technical users.
Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, and Heinrich Küttler · 2020
Earlier work this paper cites.
Intelligent document processing–methods and tools in the real world
Graham A. Cutting and Anne-Françoise Cutting-Decelle · 2021
Earlier work this paper cites.
The trip to the enterprise gourmet data product marketplace through a self-service data platform
Michal Zasadzinski, Michael Theodoulou, Markus Thurner, and Kshitij Ranganath · 2021
Earlier work this paper cites.
Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, and Dawn Drain · 2022
Earlier work this paper cites.
Improving performance of next.js app and testing it while building a badminton based web app
S. Sasikumar, S. Prabha, and Chandra Mohan · 2022
Earlier work this paper cites.
Chatgpt: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope
Partha Pratim Ray · 2023
Earlier work this paper cites.
Chatgpt for (finance) research: The bananarama conjecture
Michael Dowling and Brian Lucey · 2023
Earlier work this paper cites.
Xianzhi Li, Xiaodan Zhu, Zhiqiang Ma, Xiaomo Liu, and Sameena Shah · 2023
Cited alongside, same era.
Bhaskarjit Sarmah, Tianjie Zhu, Dhagash Mehta, and Stefano Pasquali · 2023
Cited alongside, same era.
Fingpt: Instruction tuning benchmark for open-source large language models in financial datasets
Neng Wang, Hongyang Yang, and Christina Dan Wang · 2023
Cited alongside, same era.
Pmc-llama: Towards building open-source language models for medicine
Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie · 2023
Cited alongside, same era.
Rohan Anil, Andrew M. Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, and Siamak Shakeri · 2023
Closest in time.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, and Nikolay Bashlykov · 2023
Closest in time.
Swe-bench: Can language models resolve real-world github issues?
Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan · 2023
Closest in time.
Amadeusgpt: a natural language interface for interactive animal behavioral analysis
Shaokai Ye, Jessy Lauer, Mu Zhou, Alexander Mathis, and Mackenzie W. Mathis · 2023
Closest in time.
Theory of mind for multi-agent collaboration via large language models
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Aniruddha Deb, Neeva Oza, Sarthak Singla, Dinesh Khandelwal, Dinesh Garg, and Parag Singla · 2023
Cited alongside, same era.
Fusecap: Leveraging large language models to fuse visual data into enriched image captions
Noam Rotstein, David Bensaid, Shaked Brody, Roy Ganz, and Ron Kimmel · 2023
Cited alongside, same era.
Retclean: Retrieval-based data cleaning using foundation models and data lakes
Mohammad Shahmeer Ahmad, Zan Ahmad Naeem, Mohamed Eltabakh, Mourad Ouzzani, and Nan Tang · 2023
Cited alongside, same era.
Gpt-4 technical report, 2023
OpenAI · 2023
Cited alongside, same era.
Huao Li, Yu Quan Chong, Simon Stepputtis, Joseph Campbell, Dana Hughes, Michael Lewis, and Katia Sycara · 2023
Closest in time.
Large language model-empowered agents for simulating macroeconomic activities
Nian Li, Chen Gao, Yong Li, and Qingmin Liao · 2023
Closest in time.
Exploring the intersection of large language models and agent-based modeling via prompt engineering
Edward Junprung · 2023
Closest in time.