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Code Generation
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Code Generation is an important field to predict explicit code or program structure from multimodal data sources such as incomplete code, programs in another programming language, natural language descriptions or execution examples. Code Generation tools can assist the development of automatic programming tools to improve programming productivity.
Source: Deep Learning for Source Code Modeling and Generation
Image source: Measuring Coding Challenge Competence With APPS
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dessertlab/Shellcode_IA32
공식 코드Shellcode_IA32: A Dataset for Automatic Shellcode Generation
https://github.com/dessertlab/targeted-data-poisoning-attacks
공식 코드Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks
https://github.com/devichand579/HPT
공식 코드https://github.com/dexter-gt-86/sinklora
공식 코드SinkLoRA: Enhanced Efficiency and Chat Capabilities for Long-Context Large Language Models
https://github.com/dhx20150812/toolcoder
공식 코드ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models
https://github.com/dieserlaurenz/leetcode-gym
공식 코드https://github.com/doc0318/cogito
공식 코드Cogito, ergo sum: A Neurobiologically-Inspired Cognition-Memory-Growth System for Code Generation
https://github.com/docu-mint/documint
공식 코드DocuMint: Docstring Generation for Python using Small Language Models
https://github.com/dongyh20/octopus
공식 코드Octopus: Embodied Vision-Language Programmer from Environmental Feedback
https://github.com/dos-group/LLMWorkflowGenerator
공식 코드Towards Machine-Generated Code for the Resolution of User Intentions
https://github.com/dpfried/incoder
공식 코드InCoder: A Generative Model for Code Infilling and Synthesis
https://github.com/drndr/gencodesearchnet
공식 코드https://github.com/dsaatusu/chatgpt-promises-and-pitfalls
공식 코드Assessing the Promise and Pitfalls of ChatGPT for Automated Code Generation
https://github.com/dvlab-research/motcoder
공식 코드MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging Programming Tasks
https://github.com/dynamite321/codeguardplus
공식 코드https://github.com/ease-crc/vr-neem-converter
공식 코드Knowledge-Driven Robot Program Synthesis from Human VR Demonstrations
https://github.com/ease-crc/vr-program-synthesis
공식 코드Knowledge-Driven Robot Program Synthesis from Human VR Demonstrations
https://github.com/ed741/cain
공식 코드https://github.com/effibench/effibench-x
공식 코드EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code
https://github.com/eleutherai/gpt-neox
공식 코드https://github.com/elfsong/mercury
공식 코드Mercury: A Code Efficiency Benchmark for Code Large Language Models
https://github.com/engineeringsoftware/teco
공식 코드https://github.com/epfl-dlab/gcd
공식 코드Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning
https://github.com/ernie-research/ma-rlhf
공식 코드MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions
https://github.com/eth-sri/llm-quantization-attack
공식 코드https://github.com/eth-sri/safecoder
공식 코드https://github.com/eth-sri/sven
공식 코드Large Language Models for Code: Security Hardening and Adversarial Testing
https://github.com/evalplus/evalplus
공식 코드https://github.com/evo-eval/evoeval
공식 코드https://github.com/evoagentx/evoagentx
공식 코드SEW: Self-Evolving Agentic Workflows for Automated Code Generation
https://github.com/ezelikman/parsel
공식 코드Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions
https://github.com/facebookresearch/CodeGen
공식 코드https://github.com/facebookresearch/CodeGen/blob/main/docs/dobf.md
공식 코드DOBF: A Deobfuscation Pre-Training Objective for Programming Languages
https://github.com/facebookresearch/bigobench
공식 코드BigO(Bench) -- Can LLMs Generate Code with Controlled Time and Space Complexity?
https://github.com/facebookresearch/codellama
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.