Fetching the paper…
Reading the bibliography…
We introduce API Pack, a massive multi-programming language dataset containing over one million instruction-API calls for improving the API call generation capabilities of large language models.
Application Programming Interface Documentation: What Do Software Developers Want?
Michael Meng, Stephanie Steinhardt, and Andreas Schubert · 2018
Earlier work this paper cites.
Program Synthesis with Large Language Models, August 2021
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton · 2021
Earlier work this paper cites.
Ratcliff/obershelp pattern recognition
Paul E. Black · 2021
Earlier work this paper cites.
Synchromesh: Reliable code generation from pre-trained language models
Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani · 2022
Earlier work this paper cites.
Automatic generation of programming exercises and code explanations using large language models
Sami Sarsa, Paul Denny, Arto Hellas, and Juho Leinonen · 2022
Earlier work this paper cites.
Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman · 2022
Earlier work this paper cites.
A systematic evaluation of large language models of code
Frank F Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn · 2022
Earlier work this paper cites.
When language model meets private library, 2022
Daoguang Zan, Bei Chen, Zeqi Lin, Bei Guan, Yongji Wang, and Jian-Guang Lou · 2022
Earlier work this paper cites.
Large language model assisted software engineering: prospects, challenges, and a case study
Lenz Belzner, Thomas Gabor, and Martin Wirsing · 2023
Earlier work this paper cites.
AlpaGasus: Training A Better Alpaca with Fewer Data, November 2023
Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, and Hongxia Jin · 2023
Earlier work this paper cites.
Generative AI for Software Practitioners
Christof Ebert and Panos Louridas · 2023
Earlier work this paper cites.
Large language models for software engineering: Survey and open problems
Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy, Shubho Sengupta, Shin Yoo, and Jie M Zhang · 2023
Earlier work this paper cites.
Large Language Models for Software Engineering: A Systematic Literature Review, September 2023
Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang · 2023
Earlier work this paper cites.
Let’s chat to find the apis: Connecting human, llm and knowledge graph through ai chain
Qing Huang, Zhenyu Wan, Zhenchang Xing, Changjing Wang, Jieshan Chen, Xiwei Xu, and Qinghua Lu · 2023
Earlier work this paper cites.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
Earlier work this paper cites.
Sheetcopilot: Bringing software productivity to the next level through large language models
Hongxin Li, Jingran Su, Yuntao Chen, Qing Li, and ZHAO-XIANG ZHANG · 2023
Cited alongside, same era.
Code as Policies: Language Model Programs for Embodied Control
Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng · 2023
Cited alongside, same era.
Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and LINGMING ZHANG · 2023
Cited alongside, same era.
Keming Lu, Hongyi Yuan, Zheng Yuan, Runji Lin, Junyang Lin, Chuanqi Tan, Chang Zhou, and Jingren Zhou · 2023
Cited alongside, same era.
OctoPack: Instruction Tuning Code Large Language Models, August 2023
C-Pack: Packaged Resources To Advance General Chinese Embedding, December 2023
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff · 2023
Later among the works it cites.
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction, May 2023
Rui Yang, Lin Song, Yanwei Li, Sijie Zhao, Yixiao Ge, Xiu Li, and Ying Shan · 2023
Later among the works it cites.
Large Language Models Meet NL2Code: A Survey
Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, and Jian-Guang Lou · 2023
Later among the works it cites.
Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x, 2023
Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, and Jie Tang · 2023
Later among the works it cites.
A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, and Shayne Longpre · 2023
Cited alongside, same era.
Application of large language models to software engineering tasks: Opportunities, risks, and implications
Ipek Ozkaya · 2023
Cited alongside, same era.
Gorilla: Large Language Model Connected with Massive APIs, May 2023
Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez · 2023
Cited alongside, same era.
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs, July 2023
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun · 2023
Cited alongside, same era.
The programmer’s assistant: Conversational interaction with a large language model for software development
Steven I Ross, Fernando Martinez, Stephanie Houde, Michael Muller, and Justin D Weisz · 2023
Cited alongside, same era.
Toolformer: Language Models Can Teach Themselves to Use Tools, February 2023
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
Cited alongside, same era.
Nexusraven: a commercially-permissive language model for function calling
Venkat Krishna Srinivasan, Zhen Dong, Banghua Zhu, Brian Yu, Damon Mosk-Aoyama, Kurt Keutzer, Jiantao Jiao, and Jian Zhang · 2023
Cited alongside, same era.
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases, September 2023
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, and Le Sun · 2023
Cited alongside, same era.
Closest in time.
Octopus: On-device language model for function calling of software apis
Wei Chen, Zhiyuan Li, and Mingyuan Ma · 2024
Closest in time.
On mitigating code llm hallucinations with api documentation, 2024
Nihal Jain, Robert Kwiatkowski, Baishakhi Ray, Murali Krishna Ramanathan, and Varun Kumar · 2024
Closest in time.
Starcoder 2 and the stack v2: The next generation
Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, et al · 2024
Closest in time.
Granite code models: A family of open foundation models for code intelligence
Mayank Mishra, Matt Stallone, Gaoyuan Zhang, Yikang Shen, Aditya Prasad, Adriana Meza Soria, Michele Merler, Parameswaran Selvam, Saptha Surendran, Shivdeep Singh, et al · 2024
Closest in time.
Qiwei Peng, Yekun Chai, and Xuhong Li · 2024
Closest in time.
Verigen: A large language model for verilog code generation
Shailja Thakur, Baleegh Ahmad, Hammond Pearce, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri, and Siddharth Garg · 2024
Closest in time.
Software testing with large language models: Survey, landscape, and vision
Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, and Qing Wang · 2024
Closest in time.
Easytool: Enhancing llm-based agents with concise tool instruction
Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Ren Kan, Dongsheng Li, and Deqing Yang · 2024
Closest in time.
Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, et al · 2024
Closest in time.