SOTAVerified

Program Synthesis

Program synthesis is the process of automatically generating a program or code snippet that satisfies a given specification or set of requirements. This can include generating code from a formal specification, a natural language description, or example inputs and outputs. The primary goal of program synthesis is to minimize human intervention in the coding process, reduce errors, and improve productivity.

Program synthesis often involves the use of advanced algorithms, artificial intelligence, and machine learning techniques to search the space of possible programs that meet the given constraints. This process can be guided by a variety of techniques, such as constraint solving, symbolic execution, and genetic algorithms.

Papers

Showing 150 of 423 papers

TitleStatusHype
CodeGen: An Open Large Language Model for Code with Multi-Turn Program SynthesisCode6
Gorilla: Large Language Model Connected with Massive APIsCode6
TikZero: Zero-Shot Text-Guided Graphics Program SynthesisCode5
CodeGen2: Lessons for Training LLMs on Programming and Natural LanguagesCode5
Factorio Learning EnvironmentCode4
ARC Prize 2024: Technical ReportCode3
Large Language Models Are Human-Level Prompt EngineersCode3
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code GenerationCode3
Comparison of Syntactic and Semantic Representations of Programs in Neural EmbeddingsCode3
The Surprising Effectiveness of Test-Time Training for Few-Shot LearningCode3
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement LearningCode2
Combining Induction and Transduction for Abstract ReasoningCode2
Parsel: Algorithmic Reasoning with Language Models by Composing DecompositionsCode2
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLMCode2
CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and DebuggingCode2
Searching Latent Program SpacesCode2
MapCoder: Multi-Agent Code Generation for Competitive Problem SolvingCode2
InCoder: A Generative Model for Code Infilling and SynthesisCode2
Improving Code Generation by Training with Natural Language FeedbackCode1
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learningCode1
Improving Molecular Design by Stochastic Iterative Target AugmentationCode1
Guiding Program Synthesis by Learning to Generate ExamplesCode1
Data types as a more ergonomic frontend for Grammar-Guided Genetic ProgrammingCode1
CrossBeam: Learning to Search in Bottom-Up Program SynthesisCode1
Graphs, Constraints, and Search for the Abstraction and Reasoning CorpusCode1
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human LevelCode1
PoE-World: Compositional World Modeling with Products of Programmatic ExpertsCode1
ANPL: Towards Natural Programming with Interactive DecompositionCode1
How Efficient is LLM-Generated Code? A Rigorous & High-Standard BenchmarkCode1
Enhancing Network Management Using Code Generated by Large Language ModelsCode1
Graph-based, Self-Supervised Program Repair from Diagnostic FeedbackCode1
H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus BenchmarkCode1
AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz TestingCode1
From Examples to Rules: Neural Guided Rule Synthesis for Information ExtractionCode1
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design SequencesCode1
Communicating Natural Programs to Humans and MachinesCode1
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance ComputingCode1
AutoIOT: LLM-Driven Automated Natural Language Programming for AIoT ApplicationsCode1
CodeUpdateArena: Benchmarking Knowledge Editing on API UpdatesCode1
Constrained Decoding for Fill-in-the-Middle Code Language Models via Efficient Left and Right Quotienting of Context-Sensitive GrammarsCode1
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree SearchCode1
Automatic Program Synthesis of Long Programs with a Learned Garbage CollectorCode1
CodeScholar: Growing Idiomatic Code ExamplesCode1
Automating the Design of Multigrid Methods with Evolutionary Program SynthesisCode1
Emergent Representations of Program Semantics in Language Models Trained on ProgramsCode1
Analyzing the Effectiveness of Large Language Models on Text-to-SQL SynthesisCode1
Code Building Genetic ProgrammingCode1
CodeIt: Self-Improving Language Models with Prioritized Hindsight ReplayCode1
Execution-based Code Generation using Deep Reinforcement LearningCode1
Bug In the Code Stack: Can LLMs Find Bugs in Large Python Code StacksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DrRepairSuccess rate @budget 10038.5Unverified
2Multiclass localizerSuccess rate @budget 10034.2Unverified
#ModelMetricClaimedVerifiedStatus
1DrRepairSuccess rate @budget 10057Unverified
2Multiclass localizerSuccess rate @budget 10053.7Unverified
#ModelMetricClaimedVerifiedStatus
1CodeTrans-MT-TF-SmallAccuracy90.31Unverified