Papers Program Synthesis
“Program Synthesis” 태그가 달린 논문 538편 · 필터 해제
The Program Testing Ability of Large Language Models for Code
Recent development of large language models (LLMs) for code like CodeX and CodeT5+ demonstrates tremendous promise in achieving code intelligence. Their ability of synthesizing code that completes a program for performin…
HumanEvalmbppProgram SynthesisLogic-Q: Improving Deep Reinforcement Learning-based Quantitative Trading via Program Sketch-based Tuning
Deep reinforcement learning (DRL) has revolutionized quantitative trading (Q-trading) by achieving decent performance without significant human expert knowledge. Despite its achievements, we observe that the current stat…
Deep Reinforcement LearningProgram Synthesisreinforcement-learningReinforcement LearningLLM for SoC Security: A Paradigm Shift
As the ubiquity and complexity of system-on-chip (SoC) designs increase across electronic devices, the task of incorporating security into an SoC design flow poses significant challenges. Existing security solutions are …
Natural Language UnderstandingProgram SynthesisProgram Synthesis with Best-First Bottom-Up Search
Cost-guided bottom-up search (BUS) algorithms use a cost function to guide the search to solve program synthesis tasks. In this paper, we show that current state-of-the-art cost-guided BUS algorithms suffer from a common…
Program SynthesismlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis
MLIR is an emerging compiler infrastructure for modern hardware, but existing programs cannot take advantage of MLIR's high-performance compilation if they are described in lower-level general purpose languages. Conseque…
Program Synthesis$\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis
Program synthesis aims to create accurate, executable programs from problem specifications, specifically from natural language descriptions in our context. Recent studies have leveraged the power of reinforcement learnin…
Code GenerationDeep Reinforcement LearningProgram Synthesisreinforcement-learning+1Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments
Understanding the interactions of agents trained with deep reinforcement learning is crucial for deploying agents in games or the real world. In the former, unreasonable actions confuse players. In the latter, that effec…
Atari GamesDecision MakingDeep Reinforcement LearningLanguage Modeling+4Amortizing Pragmatic Program Synthesis with Rankings
In program synthesis, an intelligent system takes in a set of user-generated examples and returns a program that is logically consistent with these examples. The usage of Rational Speech Acts (RSA) framework has been suc…
Program SynthesisLearning MDL logic programs from noisy data
Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including rec…
Drug DesignInductive logic programmingProgram SynthesisLearning logic programs by discovering higher-order abstractions
We introduce the higher-order refactoring problem, where the goal is to compress a logic program by discovering higher-order abstractions, such as map, filter, and fold. We implement our approach in Stevie, which formula…
Inductive logic programmingProgram SynthesisVisual ReasoningCorrect and Optimal: the Regular Expression Inference Challenge
We propose regular expression inference (REI) as a challenge for code/language modelling, and the wider machine learning community. REI is a supervised machine learning (ML) and program optimisation task, and poses the p…
Language ModellingProgram SynthesisDemonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example
Data management and analysis tasks are often carried out using spreadsheet software. A popular feature in most spreadsheet platforms is the ability to define data-dependent formatting rules. These rules can express actio…
ManagementProgram SynthesisEnhancing Network Management Using Code Generated by Large Language Models
Analyzing network topologies and communication graphs plays a crucial role in contemporary network management. However, the absence of a cohesive approach leads to a challenging learning curve, heightened errors, and ine…
ManagementNatural Language QueriesProgram SynthesisChatGPT for GTFS: Benchmarking LLMs on GTFS Understanding and Retrieval
The General Transit Feed Specification (GTFS) standard for publishing transit data is ubiquitous. GTFS being tabular data, with information spread across different files, necessitates specialized tools or packages to ret…
BenchmarkingInformation RetrievalMultiple-choiceProgram Synthesis+1ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis
When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neural program synthesis methods have simila…
Program SynthesisA Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis
Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web automation. However, the performance on real-world websites has still suffered from (1) open d…
Code GenerationDenoisingInductive BiasLong-Context Understanding+2GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators
A wide variety of generative models for graphs have been proposed. They are used in drug discovery, road networks, neural architecture search, and program synthesis. Generating graphs has theoretical challenges, such as …
Drug DiscoveryGraph EmbeddingNeural Architecture SearchProgram SynthesisReinforcement Learning and Data-Generation for Syntax-Guided Synthesis
Program synthesis is the task of automatically generating code based on a specification. In Syntax-Guided Synthesis (SyGuS) this specification is a combination of a syntactic template and a logical formula, and the resul…
Program Synthesisreinforcement-learningReinforcement LearningRLTF: Reinforcement Learning from Unit Test Feedback
The goal of program synthesis, or code generation, is to generate executable code based on given descriptions. Recently, there has been an increasing number of studies employing reinforcement learning (RL) to improve the…
Code GenerationmbppProgram Synthesisreinforcement-learning+2Multi-Intent Detection in User Provided Annotations for Programming by Examples Systems
In mapping enterprise applications, data mapping remains a fundamental part of integration development, but its time consuming. An increasing number of applications lack naming standards, and nested field structures furt…
Intent DetectionProgram Synthesis