{"list":"other","total":52,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"2107.03374","title":"Evaluating Large Language Models Trained on Code","created":"2021-07-07","cited_by_count":8320},{"id":"1409.3215","title":"Sequence to Sequence Learning with Neural Networks","created":"2014-09-10","cited_by_count":2986},{"id":"2305.01210","title":"Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation","created":"2023-05-02","cited_by_count":1391},{"id":"2105.09938","title":"Measuring Coding Challenge Competence With APPS","created":"2021-05-20","cited_by_count":902},{"id":"2102.04664","title":"CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation","created":"2021-02-09","cited_by_count":744},{"id":"1909.09436","title":"CodeSearchNet Challenge: Evaluating the State of Semantic Code Search","created":"2019-09-20","cited_by_count":654},{"id":"2302.11382","title":"A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT","created":"2023-02-21","cited_by_count":619},{"id":"2102.07350","title":"Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm","created":"2021-02-15","cited_by_count":606},{"id":"1909.03496","title":"Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks","created":"2019-09-08","cited_by_count":272},{"id":"2204.02329","title":"Can language models learn from explanations in context?","created":"2022-04-05","cited_by_count":249},{"id":"2303.05398","title":"MathPrompter: Mathematical Reasoning using Large Language Models","created":"2023-03-04","cited_by_count":236},{"id":"2304.04370","title":"OpenAGI: When LLM Meets Domain Experts","created":"2023-04-10","cited_by_count":197},{"id":"2206.15331","title":"GitHub Copilot AI pair programmer: Asset or Liability?","created":"2022-06-30","cited_by_count":152},{"id":"1901.01808","title":"SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair","created":"2018-12-24","cited_by_count":145},{"id":"2009.07235","title":"Deep Learning based Vulnerability Detection: Are We There Yet?","created":"2020-09-03","cited_by_count":144},{"id":"2210.14179","title":"Practical Program Repair in the Era of Large Pre-trained Language Models","created":"2022-10-25","cited_by_count":133},{"id":"2304.00385","title":"Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT","created":"2023-04-01","cited_by_count":125},{"id":"2303.07263","title":"InferFix: End-to-End Program Repair with LLMs","created":"2023-03-13","cited_by_count":114},{"id":"2305.04087","title":"Self-Edit: Fault-Aware Code Editor for Code Generation","created":"2023-05-06","cited_by_count":111},{"id":"2301.08653","title":"An Analysis of the Automatic Bug Fixing Performance of ChatGPT","created":"2023-01-20","cited_by_count":110},{"id":"2112.02125","title":"Examining Zero-Shot Vulnerability Repair with Large Language Models","created":"2021-12-03","cited_by_count":105},{"id":"2112.02969","title":"Jigsaw: Large Language Models meet Program Synthesis","created":"2021-12-06","cited_by_count":103},{"id":"2212.14834","title":"Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models","created":"2022-12-30","cited_by_count":103},{"id":"2103.00073","title":"CURE: Code-Aware Neural Machine Translation for Automatic Program Repair","created":"2021-02-26","cited_by_count":86},{"id":"2207.08281","title":"Less Training, More Repairing Please: Revisiting Automated Program Repair via Zero-shot Learning","created":"2022-07-17","cited_by_count":76},{"id":"2304.11938","title":"Is ChatGPT the Ultimate Programming Assistant -- How far is it?","created":"2023-04-24","cited_by_count":76},{"id":"2106.06600","title":"Break-It-Fix-It: Unsupervised Learning for Program Repair","created":"2021-06-11","cited_by_count":71},{"id":"2205.10583","title":"Automated Repair of Programs from Large Language Models","created":"2022-05-21","cited_by_count":71},{"id":"2208.11640","title":"Repair Is Nearly Generation: Multilingual Program Repair with LLMs","created":"2022-08-24","cited_by_count":68},{"id":"2106.08253","title":"A Syntax-Guided Edit Decoder for Neural Program Repair","created":"2021-06-15","cited_by_count":57},{"id":"2206.07585","title":"NatGen: Generative pre-training by \"Naturalizing\" source code","created":"2022-06-15","cited_by_count":54},{"id":"2203.05132","title":"Compilable Neural Code Generation with Compiler Feedback","created":"2022-03-10","cited_by_count":47},{"id":"1902.06111","title":"Getafix: Learning to Fix Bugs Automatically","created":"2019-02-16","cited_by_count":41},{"id":"2301.13246","title":"Conversational Automated Program Repair","created":"2023-01-30","cited_by_count":41},{"id":"2104.08308","title":"Neural Transfer Learning for Repairing Security Vulnerabilities in C Code","created":"2021-04-16","cited_by_count":36},{"id":"2304.02014","title":"Large Language Models are Edge-Case Fuzzers: Testing Deep Learning Libraries via FuzzGPT","created":"2023-04-04","cited_by_count":36},{"id":"1707.04742","title":"Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities","created":"2017-07-15","cited_by_count":26},{"id":"2304.08191","title":"A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repair","created":"2023-04-17","cited_by_count":26},{"id":"2111.03922","title":"Automatic Program Repair with OpenAI's Codex: Evaluating QuixBugs","created":"2021-11-06","cited_by_count":25},{"id":"2205.01859","title":"DEAR: A Novel Deep Learning-based Approach for Automated Program Repair","created":"2022-05-04","cited_by_count":22},{"id":"2207.11765","title":"Neurosymbolic Repair for Low-Code Formula Languages","created":"2022-07-24","cited_by_count":12},{"id":"2302.03780","title":"Reliable Natural Language Understanding with Large Language Models and Answer Set Programming","created":"2023-02-07","cited_by_count":12},{"id":"2303.17511","title":"On pitfalls (and advantages) of sophisticated large language models","created":"2023-02-25","cited_by_count":7},{"id":"1810.12041","title":"SMT-Based Refutation of Spurious Bug Reports in the Clang Static Analyzer","created":"2018-10-29","cited_by_count":5},{"id":"2007.15943","title":"MUZZ: Thread-aware Grey-box Fuzzing for Effective Bug Hunting in Multithreaded Programs","created":"2020-07-31","cited_by_count":3},{"id":"2107.01093","title":"Model Checking C++ Programs","created":"2021-07-02","cited_by_count":3},{"id":"2206.06043","title":"Combining BMC and Fuzzing Techniques for Finding Software Vulnerabilities in Concurrent Programs","created":"2022-06-13","cited_by_count":2},{"id":"2111.13117","title":"ESBMC-Solidity: An SMT-Based Model Checker for Solidity Smart Contracts","created":"2021-11-25","cited_by_count":1}],"next_cursor":null}