{"total":32,"rows":[{"id":"2210.08726","title":"RARR: Researching and Revising What Language Models Say, Using Language Models","created":"2022-10-17","cited_by_count":287,"area":null},{"id":"2207.05987","title":"DocPrompting: Generating Code by Retrieving the Docs","created":"2022-07-13","cited_by_count":153,"area":null},{"id":"2303.10868","title":"Retrieving Multimodal Information for Augmented Generation: A Survey","created":"2023-03-20","cited_by_count":85,"area":null},{"id":"2105.03654","title":"Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning","created":"2021-05-08","cited_by_count":76,"area":null},{"id":"2201.12431","title":"Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval","created":"2022-01-28","cited_by_count":60,"area":null},{"id":"2208.11126","title":"Retrieval-based Controllable Molecule Generation","created":"2022-08-23","cited_by_count":44,"area":null},{"id":"2405.02213","title":"Automatic Programming: Large Language Models and Beyond","created":"2024-05-03","cited_by_count":36,"area":null},{"id":"2110.08458","title":"Controllable Semantic Parsing via Retrieval Augmentation","created":"2021-10-16","cited_by_count":35,"area":null},{"id":"2308.10462","title":"Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models","created":"2023-08-21","cited_by_count":28,"area":null},{"id":"2401.00288","title":"Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit","created":"2023-12-30","cited_by_count":22,"area":null},{"id":"2104.13100","title":"Shellcode_IA32: A Dataset for Automatic Shellcode Generation","created":"2021-04-27","cited_by_count":16,"area":null},{"id":"2208.10091","title":"Incorporating Domain Knowledge through Task Augmentation for Front-End JavaScript Code Generation","created":"2022-08-22","cited_by_count":11,"area":null},{"id":"2106.00261","title":"Exploring Dynamic Selection of Branch Expansion Orders for Code Generation","created":"2021-06-01","cited_by_count":10,"area":null},{"id":"2311.12890","title":"De-fine: Decomposing and Refining Visual Programs with Auto-Feedback","created":"2023-11-21","cited_by_count":10,"area":null},{"id":"2409.15204","title":"RAMBO: Enhancing RAG-based Repository-Level Method Body Completion","created":"2024-09-23","cited_by_count":10,"area":null},{"id":"2504.08734","title":"Towards an Understanding of Context Utilization in Code Intelligence","created":"2025-04-11","cited_by_count":10,"area":null},{"id":"2106.04447","title":"Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation","created":"2021-06-08","cited_by_count":8,"area":null},{"id":"2209.10254","title":"T5QL: Taming language models for SQL generation","created":"2022-09-21","cited_by_count":8,"area":null},{"id":"2509.10697","title":"A Survey on Retrieval And Structuring Augmented Generation with Large Language Models","created":"2025-09-12","cited_by_count":8,"area":null},{"id":"2212.10692","title":"Generation-Augmented Query Expansion For Code Retrieval","created":"2022-12-20","cited_by_count":6,"area":null},{"id":"2409.05923","title":"$\\mathbb{USCD}$: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding","created":"2024-09-09","cited_by_count":6,"area":null},{"id":"2101.03999","title":"A Neural Question Answering System for Basic Questions about Subroutines","created":"2021-01-11","cited_by_count":5,"area":null},{"id":"2103.15361","title":"Embedding API Dependency Graph for Neural Code Generation","created":"2021-03-29","cited_by_count":5,"area":null},{"id":"2108.12144","title":"Lyra: A Benchmark for Turducken-Style Code Generation","created":"2021-08-27","cited_by_count":5,"area":null},{"id":"2108.10763","title":"ComSum: Commit Messages Summarization and Meaning Preservation","created":"2021-08-23","cited_by_count":3,"area":null},{"id":"2206.13354","title":"Transformer with Tree-order Encoding for Neural Program Generation","created":"2022-05-30","cited_by_count":3,"area":null},{"id":"2112.11858","title":"End to End Software Engineering Research","created":"2021-12-22","cited_by_count":2,"area":null},{"id":"2203.15319","title":"Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation","created":"2022-03-29","cited_by_count":2,"area":null},{"id":"2503.23231","title":"CCCI: Code Completion with Contextual Information for Complex Data Transfer Tasks Using Large Language Models","created":"2025-03-29","cited_by_count":2,"area":null},{"id":"2604.17184","title":"SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair","created":"2026-04-19","cited_by_count":1,"area":null},{"id":"2202.03755","title":"Can We Generate Shellcodes via Natural Language? An Empirical Study","created":"2022-02-08","cited_by_count":0,"area":null},{"id":"2208.09998","title":"Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation","created":"2022-08-22","cited_by_count":0,"area":null}],"next_cursor":null}