Fetching the paper…
Reading the bibliography…
Recent research in Needle-in-a-Haystack (NIAH) benchmarks has explored the capabilities of Large Language Models (LLMs) in retrieving contextual information from large text documents.
Program synthesis
Sumit Gulwani, Oleksandr Polozov, Rishabh Singh, et al · 2017
Earlier work this paper cites.
Language models are few-shot learners, 2020
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
Earlier work this paper cites.
Github copilot, 2021
GitHub · 2021
Earlier work this paper cites.
Evaluating large language models trained on code, 2021
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
Earlier work this paper cites.
Needle in a haystack - pressure testing llms, 2023
Greg Kamradt · 2023
Earlier work this paper cites.
Codegen: An open large language model for code with multi-turn program synthesis, 2023
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2023
Earlier work this paper cites.
Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation, 2023
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang · 2023
Earlier work this paper cites.
python-code-instructions-18k-alpaca, 2023
Tarun Bisht · 2023
Earlier work this paper cites.
Litellm, 2023
BerriAI · 2023
Earlier work this paper cites.
Gpt-4 technical report, 2024
OpenAI · 2024
Cited alongside, same era.
Introducing the next generation of claude anthropic, 2024
Anthropic · 2024
Cited alongside, same era.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context, 2024
Google Team Gemini · 2024
Cited alongside, same era.
Fables: Evaluating faithfulness and content selection in book-length summarization, 2024
Yekyung Kim, Yapei Chang, Marzena Karpinska, Aparna Garimella, Varun Manjunatha, Kyle Lo, Tanya Goyal, and Mohit Iyyer · 2024
Cited alongside, same era.
Llm vs. lawyers: Identifying a subset of summary judgments in a large uk case law dataset, 2024
Ahmed Izzidien, Holli Sargeant, and Felix Steffek · 2024
Cited alongside, same era.
Capabilities of gemini models in medicine, 2024
Khaled Saab, Tao Tu, Wei-Hung Weng, Ryutaro Tanno, David Stutz, Ellery Wulczyn, Fan Zhang, Tim Strother, Chunjong Park, Elahe Vedadi, Juanma Zambrano Chaves, Szu-Yeu Hu, Mike Schaekermann, Aishwarya Kamath, Yong Cheng, David G. T. Barrett, Cathy Cheung, Basil Mustafa, Anil Palepu, Daniel McDuff, Le Hou, Tomer Golany, Luyang Liu, Jean baptiste Alayrac, Neil Houlsby, Nenad Tomasev, Jan Freyberg, Charles Lau, Jonas Kemp, Jeremy Lai, Shekoofeh Azizi, Kimberly Kanada, SiWai Man, Kavita Kulkarni, Ruoxi Sun, Siamak Shakeri, Luheng He, Ben Caine, Albert Webson, Natasha Latysheva, Melvin Johnson, Philip Mansfield, Jian Lu, Ehud Rivlin, Jesper Anderson, Bradley Green, Renee Wong, Jonathan Krause, Jonathon Shlens, Ewa Dominowska, S. M. Ali Eslami, Katherine Chou, Claire Cui, Oriol Vinyals, Koray Kavukcuoglu, James Manyika, Jeff Dean, Demis Hassabis, Yossi Matias, Dale Webster, Joelle Barral, Greg Corrado, Christopher Semturs, S. Sara Mahdavi, Juraj Gottweis, Alan Karthikesalingam, and Vivek Natarajan · 2024
Repoqa: Evaluating long context code understanding, 2024
Jiawei Liu, Jia Le Tian, Vijay Daita, Yuxiang Wei, Yifeng Ding, Yuhan Katherine Wang, Jun Yang, and Lingming Zhang · 2024
Closest in time.
In search of needles in a 11m haystack: Recurrent memory finds what llms miss, 2024
Yuri Kuratov, Aydar Bulatov, Petr Anokhin, Dmitry Sorokin, Artyom Sorokin, and Mikhail Burtsev · 2024
Closest in time.
Lv-eval: A balanced long-context benchmark with 5 length levels up to 256k, 2024
Tao Yuan, Xuefei Ning, Dong Zhou, Zhijie Yang, Shiyao Li, Minghui Zhuang, Zheyue Tan, Zhuyu Yao, Dahua Lin, Boxun Li, Guohao Dai, Shengen Yan, and Yu Wang · 2024
Closest in time.
Ruler: What’s the real context size of your long-context language models?, 2024
Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, Yang Zhang, and Boris Ginsburg · 2024
Closest in time.
Learning-based widget matching for migrating gui test cases
Yakun Zhang, Wenjie Zhang, Dezhi Ran, Qihao Zhu, Chengfeng Dou, Dan Hao, Tao Xie, and Lu Zhang · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Exploring and evaluating hallucinations in llm-powered code generation, 2024
Fang Liu, Yang Liu, Lin Shi, Houkun Huang, Ruifeng Wang, Zhen Yang, Li Zhang, Zhongqi Li, and Yuchi Ma · 2024
Cited alongside, same era.
Using an llm to help with code understanding
Daye Nam, Andrew Macvean, Vincent Hellendoorn, Bogdan Vasilescu, and Brad Myers · 2024
Cited alongside, same era.
Command r: Retrieval-augmented generation at production scale, 2024
Cohere · 2024
Closest in time.
Introducing meta llama 3: The most capable openly available llm to date, 2024
Meta · 2024
Closest in time.
A model fluent in 80+ programming languages, 2024
Mistral · 2024
Closest in time.