代码大模型 / AI 编程

Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics

Zhengde Zhang, Yiyu Zhang, Haodong Yao, Jianwen Luo, Rui Zhao, Bo Huang, Jiameng Zhao, Yipu Liao, Ke Li, Lina Zhao, Jun Cao, Fazhi Qi, Changzheng Yuan

专题命中 代码生成 ：code generation(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments 15 pages, 8 figures

Synergy of Large Language Model and Model Driven Engineering for Automated Development of Centralized Vehicular Systems

Nenad Petrovic, Fengjunjie Pan, Krzysztof Lebioda, Vahid Zolfaghari, Sven Kirchner, Nils Purschke, Muhammad Aqib Khan, Viktor Vorobev, Alois Knoll

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.CL、cs.AI

Testing the Effect of Code Documentation on Large Language Model Code Understanding

William Macke, Michael Doyle

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.CL、cs.AI

Comments 7 pages, 5 figures, 2 tables. Accepted as a Findings paper in the "Generation" track to NAACL 2024. MITRE Public Release Case Number 23-4132

Advancing LLM Reasoning Generalists with Preference Trees

Lifan Yuan, Ganqu Cui, Hanbin Wang, Ning Ding, Xingyao Wang, Jia Deng, Boji Shan, Huimin Chen, Ruobing Xie, Yankai Lin, Zhenghao Liu, Bowen Zhou, Hao Peng, Zhiyuan Liu, Maosong Sun

专题命中 代码生成 ：code generation(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Models and data are available at https://github.com/OpenBMB/Eurus

Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models

Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel

专题命中 代码生成 ：code generation(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments 34 pages, 10 figures, published as conference paper at ICLR 2024, and accepted to the Socially Responsible Language Modelling Research (SoLaR) workshop at NeurIPS 2023

SoD$^2$: Statically Optimizing Dynamic Deep Neural Network

Wei Niu, Gagan Agrawal, Bin Ren

专题命中 代码生成 ：code generation(abstract)；分类 cs.AI、cs.LG、cs.PL

The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations?

Alex Gu, Wen-Ding Li, Naman Jain, Theo X. Olausson, Celine Lee, Koushik Sen, Armando Solar-Lezama

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.AI、cs.LG

Comments 54 pages, 25 figures

A Machine Learning Approach Towards SKILL Code Autocompletion

Enrique Dehaerne, Bappaditya Dey, Wannes Meert

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.CL、cs.PL

Comments Accepted for SPIE Advanced Lithography + Patterning, 2024

Are ChatGPT and Other Similar Systems the Modern Lernaean Hydras of AI?

Dimitrios Ioannidis, Jeremy Kepner, Andrew Bowne, Harriet S. Bryant

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.AI、cs.LG

Comments 40 pages, 100+ references, to appear in Fordham Law Review

Learning logic programs by discovering higher-order abstractions

Céline Hocquette, Sebastijan Dumančić, Andrew Cropper

专题命中 代码生成 ：program synthesis(abstract)；分类 cs.AI、cs.LG、cs.PL

Large Language Models Should Ask Clarifying Questions to Increase Confidence in Generated Code

Jie JW Wu

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.AI、cs.LG

Comments 6 pages, 2 figures, 1 table. Accepted and presented at the 7th Annual Symposium on Machine Programming (MAPS 2023 Workshop, see https://mapsworkshop.github.io/). Reference: "Wu, Jie JW. Large Language Models Should Ask Clarifying Questions to Increase Confidence in Generated Code. The 7th Annual Symposium on Machine Programming (MAPS 23), December 3, 2023, San Francisco, CA, USA"

Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning

Saibo Geng, Martin Josifoski, Maxime Peyrard, Robert West

专题命中 代码生成 ：code generation(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Accepted at EMNLP 2023 Main Conference

Lightweight reranking for language model generations

Siddhartha Jain, Xiaofei Ma, Anoop Deoras, Bing Xiang

专题命中 代码生成 ：code generation(abstract)；分类 cs.CL、cs.AI、cs.LG

LLM Augmented LLMs: Expanding Capabilities through Composition

Rachit Bansal, Bidisha Samanta, Siddharth Dalmia, Nitish Gupta, Shikhar Vashishth, Sriram Ganapathy, Abhishek Bapna, Prateek Jain, Partha Talukdar

专题命中 代码生成 ：code generation(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments 17 pages, 2 figures, 8 tables

Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models

Terry Yue Zhuo, Armel Zebaze, Nitchakarn Suppattarachai, Leandro von Werra, Harm de Vries, Qian Liu, Niklas Muennighoff

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.CL、cs.AI

Comments 25 pages (12 main), 19 figures, 8 tables

CodeScholar: Growing Idiomatic Code Examples

Manish Shetty, Koushik Sen, Ion Stoica

专题命中 代码生成 ：program synthesis(abstract)；分类 cs.SE、cs.LG、cs.PL

Coarse-Tuning Models of Code with Reinforcement Learning Feedback

Abhinav Jain, Chima Adiole, Swarat Chaudhuri, Thomas Reps, Chris Jermaine

专题命中 代码生成 ：program synthesis(abstract)；分类 cs.AI、cs.LG、cs.PL

Comments 23 pages

Exploring Distributional Shifts in Large Language Models for Code Analysis

Shushan Arakelyan, Rocktim Jyoti Das, Yi Mao, Xiang Ren

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.CL、cs.LG

EvoPrompting: Language Models for Code-Level Neural Architecture Search

Angelica Chen, David M. Dohan, David R. So

专题命中 代码生成 ：code generation(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments NeurIPS 2023

Conceptual Model Interpreter for Large Language Models

Felix Härer

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.AI、cs.PL

Comments ER Forum 2023, 42nd International Conference on Conceptual Modeling (ER 2023), November 6-9, 2023, Lisbon, PT

ChatGPT for GTFS: Benchmarking LLMs on GTFS Understanding and Retrieval

Saipraneeth Devunuri, Shirin Qiam, Lewis Lehe

专题命中 代码生成 ：program synthesis(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments 22 pages, 8 figures, 1 table, Public Transport

FormaT5: Abstention and Examples for Conditional Table Formatting with Natural Language

Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le, Carina Negreanu, Elnaz Nouri, Mohammad Raza, Gust Verbruggen

专题命中 代码生成 ：code generation(abstract)；分类 cs.CL、cs.AI、cs.PL

Comments Contains inappropriately sourced conjecture of OpenAI's ChatGPT parameter count from www.forbes.com/sites/forbestechcouncil/2023/02/17/is-bigger-better-why-the-chatgpt-vs-gpt-3-vs-gpt-4-battle-is-just-a-family-chat, a citation which was omitted. The authors do not have direct knowledge or verification of this information, and relied solely on this article, which may lead to public confusion

How is ChatGPT's behavior changing over time?

Lingjiao Chen, Matei Zaharia, James Zou

专题命中 代码生成 ：code generation(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments add more evaluations on instruction following

SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models

Hongxin Li, Jingran Su, Yuntao Chen, Qing Li, Zhaoxiang Zhang

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.CL、cs.AI

Comments Accepted to NeurIPS 2023

TST$^\mathrm{R}$: Target Similarity Tuning Meets the Real World

Anirudh Khatry, Sumit Gulwani, Priyanshu Gupta, Vu Le, Ananya Singha, Mukul Singh, Gust Verbruggen

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.CL、cs.AI

Comments Accepted for EMNLP-Findings, 2023

Towards the Imagenets of ML4EDA

Animesh Basak Chowdhury, Shailja Thakur, Hammond Pearce, Ramesh Karri, Siddharth Garg

专题命中 代码生成 ：code generation(abstract)；分类 cs.AI、cs.LG、cs.PL

Comments Invited paper, ICCAD 2023

Journal ref ICCAD 2023

Mistral 7B

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, William El Sayed

专题命中 代码生成 ：code generation(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Models and code are available at https://mistral.ai/news/announcing-mistral-7b/

Unveiling the potential of large language models in generating semantic and cross-language clones

Palash R. Roy, Ajmain I. Alam, Farouq Al-omari, Banani Roy, Chanchal K. Roy, Kevin A. Schneider

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.AI、cs.LG

Comments Accepted in IWSC

Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables

Jian Gu, Harald C. Gall

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.LG、cs.PL

Comments 5 pages, 3 figures, 2 tables, accepted by ESEC/FSE 2023, the camera-ready version

Code Detection for Hardware Acceleration Using Large Language Models

Pablo Antonio Martínez, Gregorio Bernabé, José Manuel García

专题命中 代码生成 ：code generation(abstract)；分类 cs.SE、cs.LG、cs.PL