Papers Program Synthesis
“Program Synthesis” 태그가 달린 논문 538편 · 필터 해제
Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets
We study the ability of pretrained large language models (LLM) to answer questions from online question answering fora such as Stack Overflow. We consider question-answer pairs where the main part of the answer consists …
Code GenerationLanguage ModellingProgram SynthesisPrompt Engineering+2Bayesian Program Learning by Decompiling Amortized Knowledge
DreamCoder is an inductive program synthesis system that, whilst solving problems, learns to simplify search in an iterative wake-sleep procedure. The cost of search is amortized by training a neural search policy, reduc…
Program inductionProgram SynthesisSolving Novel Program Synthesis Problems with Genetic Programming using Parametric Polymorphism
Contemporary genetic programming (GP) systems for general program synthesis have been primarily concerned with evolving programs that can manipulate values from a standard set of primitive data types and simple indexed d…
Program SynthesisNatural Language Commanding via Program Synthesis
We present Semantic Interpreter, a natural language-friendly AI system for productivity software such as Microsoft Office that leverages large language models (LLMs) to execute user intent across application features. Wh…
Program SynthesisRetrievalKnowledge-Driven Robot Program Synthesis from Human VR Demonstrations
Aging societies, labor shortages and increasing wage costs call for assistance robots capable of autonomously performing a wide array of real-world tasks. Such open-ended robotic manipulation requires not only powerful k…
Code GenerationCommon Sense ReasoningProgram SynthesisLambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas
Search is an important technique in program synthesis that allows for adaptive strategies such as focusing on particular search directions based on execution results. Several prior works have demonstrated that neural mod…
Program SynthesisBetter Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion
Pretrained code language models have enabled great progress towards program synthesis. However, common approaches only consider in-file local context and thus miss information and constraints imposed by other parts of th…
Code CompletionProgram SynthesisSearch-Based Regular Expression Inference on a GPU
Regular expression inference (REI) is a supervised machine learning and program synthesis problem that takes a cost metric for regular expressions, and positive and negative examples of strings as input. It outputs a reg…
CPUGPUProgram SynthesisANPL: Towards Natural Programming with Interactive Decomposition
Though LLMs are capable of generating plausible programs, it's challenging to interact with the LLMs further to revise the program, especially if the user's specific requirements are different from the initial proposal. …
ARCCode GenerationHumanEvalProgram SynthesisLearning-Based Automatic Synthesis of Software Code and Configuration
Increasing demands in software industry and scarcity of software engineers motivates researchers and practitioners to automate the process of software generation and configuration. Large scale automatic software generati…
Program SynthesisCoarse-Tuning Models of Code with Reinforcement Learning Feedback
Large Language Models (LLMs) pre-trained on code have recently emerged as the dominant approach to program synthesis. However, these models are trained using next-token prediction, which ignores the syntax and semantics …
Program Synthesisreinforcement-learningReinforcement LearningGorilla: Large Language Model Connected with Massive APIs
Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis. However, their potential to effective…
HallucinationLanguage ModelingLanguage ModellingLarge Language Model+4CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts, generating flawed code, or creating of…
Fact CheckingNatural QuestionsProgram SynthesisQuestion Answering+2Probabilistic Lexicase Selection
Lexicase selection is a widely used parent selection algorithm in genetic programming, known for its success in various task domains such as program synthesis, symbolic regression, and machine learning. Due to its non-pa…
Program SynthesisregressionSymbolic RegressionEmergent Representations of Program Semantics in Language Models Trained on Programs
We present evidence that language models (LMs) of code can learn to represent the formal semantics of programs, despite being trained only to perform next-token prediction. Specifically, we train a Transformer model on a…
Inductive BiasLanguage ModellingProgram SynthesisLearning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation
Detecting parallelizable code regions is a challenging task, even for experienced developers. Numerous recent studies have explored the use of machine learning for code analysis and program synthesis, including paralleli…
Program SynthesisCodeGen2: Lessons for Training LLMs on Programming and Natural Languages
Large language models (LLMs) have demonstrated remarkable abilities in representation learning for program synthesis and understanding tasks. The quality of the learned representations appears to be dictated by the neura…
Causal Language ModelingDecoderLanguage ModelingLanguage Modelling+2From Words to Code: Harnessing Data for Program Synthesis from Natural Language
Creating programs to correctly manipulate data is a difficult task, as the underlying programming languages and APIs can be challenging to learn for many users who are not skilled programmers. Large language models (LLMs…
Program SynthesisRerankingA Survey of Methods for Converting Unstructured Data to CSG Models
The goal of this document is to survey existing methods for recovering CSG representations from unstructured data such as 3D point-clouds or polygon meshes. We review and discuss related topics such as the segmentation a…
Program SynthesisIs Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation
Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code. Programming benchmarks, with curated synthesis problems and test-cases,…
Code GenerationHumanEvalProgram Synthesis