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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Steering Conceptual Bias via Transformer Latent-Subspace Activation
RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
TeXpert: A Multi-Level Benchmark for Evaluating LaTeX Code Generation by LLMs
cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree
Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification
Sampling from Your Language Model One Byte at a Time
Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team
Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees
Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality
A Technical Study into Small Reasoning Language Models
FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation
LocationReasoner: Evaluating LLMs on Real-World Site Selection Reasoning
How Does LLM Reasoning Work for Code? A Survey and a Call to Action
Humanity's Last Code Exam: Can Advanced LLMs Conquer Human's Hardest Code Competition?
Structured Program Synthesis using LLMs: Results and Insights from the IPARC Challenge
The Safety Reminder: A Soft Prompt to Reactivate Delayed Safety Awareness in Vision-Language Models
ReVeal: Self-Evolving Code Agents via Iterative Generation-Verification
code_transformed: The Influence of Large Language Models on Code
PRO-V: An Efficient Program Generation Multi-Agent System for Automatic RTL Verification
SWE-Bench-CL: Continual Learning for Coding Agents
Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications
Execution Guided Line-by-Line Code Generation
SWE-Factory: Your Automated Factory for Issue Resolution Training Data and Evaluation Benchmarks
AutoMind: Adaptive Knowledgeable Agent for Automated Data Science
LLM-as-a-Judge for Reference-less Automatic Code Validation and Refinement for Natural Language to Bash in IT Automation
Reasoning as a Resource: Optimizing Fast and Slow Thinking in Code Generation Models
Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA
ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs
Prompt Variability Effects On LLM Code Generation
Mutual-Supervised Learning for Sequential-to-Parallel Code Translation
Understanding Software Engineering Agents Through the Lens of Traceability: An Empirical Study
Edit Flows: Flow Matching with Edit Operations
UTBoost: Rigorous Evaluation of Coding Agents on SWE-Bench
Technical Report for Argoverse2 Scenario Mining Challenges on Iterative Error Correction and Spatially-Aware Prompting
Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research
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