Papers Program Synthesis
“Program Synthesis” 태그가 달린 논문 538편 · 필터 해제
Program Synthesis using Inductive Logic Programming for the Abstraction and Reasoning Corpus
The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that is currently unsolvable by any Machine Learning method, including Large Language Models (LLMs). It demands strong generalizat…
ARCInductive logic programmingProgram SynthesisLarge Language Models Synergize with Automated Machine Learning
Recently, program synthesis driven by large language models (LLMs) has become increasingly popular. However, program synthesis for machine learning (ML) tasks still poses significant challenges. This paper explores a nov…
AutoMLCode GenerationProgram SynthesisPhilHumans: Benchmarking Machine Learning for Personal Health
The use of machine learning in Healthcare has the potential to improve patient outcomes as well as broaden the reach and affordability of Healthcare. The history of other application areas indicates that strong benchmark…
Action AnticipationBenchmarkingLanguage ModelingLanguage Modelling+1Can humans teach machines to code?
The goal of inductive program synthesis is for a machine to automatically generate a program from user-supplied examples. A key underlying assumption is that humans can provide sufficient examples to teach a concept to a…
Program SynthesisBANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming
Over the past decade, deep learning helped solve manipulation problems across all domains of robotics. At the same time, industrial robots continue to be programmed overwhelmingly using traditional program representation…
Industrial RobotsProgram SynthesisFewer Truncations Improve Language Modeling
In large language model training, input documents are typically concatenated together and then split into sequences of equal length to avoid padding tokens. Despite its efficiency, the concatenation approach compromises …
Combinatorial OptimizationHallucinationLanguage ModelingLanguage Modelling+3Self-Training Large Language Models for Improved Visual Program Synthesis With Visual Reinforcement
Visual program synthesis is a promising approach to exploit the reasoning abilities of large language models for compositional computer vision tasks. Previous work has used few-shot prompting with frozen LLMs to synthesi…
Image-text Retrievalobject-detectionObject DetectionProgram Synthesis+3Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM
LLMs have become the go-to choice for code generation tasks, with an exponential increase in the training, development, and usage of LLMs specifically for code generation. To evaluate the ability of LLMs on code, both ac…
Code GenerationHumanEvalInstruction FollowingProgram SynthesisSYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine
This paper addresses the problem of preference learning, which aims to align robot behaviors through learning user specific preferences (e.g. "good pull-over location") from visual demonstrations. Despite its similarity …
Autonomous DrivingOut-of-Distribution GeneralizationProgram SynthesisWatChat: Explaining perplexing programs by debugging mental models
Often, a good explanation for a program's unexpected behavior is a bug in the programmer's code. But sometimes, an even better explanation is a bug in the programmer's mental model of the language or API they are using. …
counterfactualLanguage ModellingMisconceptionsProgram SynthesisGuiding Enumerative Program Synthesis with Large Language Models
Pre-trained Large Language Models (LLMs) are beginning to dominate the discourse around automatic code generation with natural language specifications. In contrast, the best-performing synthesizers in the domain of forma…
Code GenerationProgram SynthesisSemi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models
Instruction tuning plays a pivotal role in Code Large Language Models (Code LLMs) for the task of program synthesis. Presently, two dominant paradigms for collecting tuning data are natural-instruct (human-written) and s…
DiversityProgram SynthesisConstrained Decoding for Fill-in-the-Middle Code Language Models via Efficient Left and Right Quotienting of Context-Sensitive Grammars
Large Language Models are powerful tools for program synthesis and advanced auto-completion, but come with no guarantee that their output code is syntactically correct. This paper contributes an incremental parser that a…
Program SynthesisProcedural Adherence and Interpretability Through Neuro-Symbolic Generative Agents
The surge in popularity of large language models (LLMs) has opened doors for new approaches to the creation of interactive agents. However, managing and interpreting the temporal behavior of such agents over the course o…
Formal LogicProgram SynthesisOrigami: (un)folding the abstraction of recursion schemes for program synthesis
Program synthesis with Genetic Programming searches for a correct program that satisfies the input specification, which is usually provided as input-output examples. One particular challenge is how to effectively handle …
Program SynthesisHumanEval on Latest GPT Models -- 2024
In 2023, we are using the latest models of GPT-4 to advance program synthesis. The large language models have significantly improved the state-of-the-art for this purpose. To make these advancements more accessible, we h…
Code GenerationHumanEvalLanguage ModelingLanguage Modelling+1LTL learning on GPUs
Linear temporal logic (LTL) is widely used in industrial verification. LTL formulae can be learned from traces. Scaling LTL formula learning is an open problem. We implement the first GPU-based LTL learner using a novel …
GPUProgram SynthesisWorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment
We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment. The world model tries to explain its interactions, while also being optimis…
Program SynthesisTask PlanningSwissNYF: Tool Grounded LLM Agents for Black Box Setting
While Large Language Models (LLMs) have demonstrated enhanced capabilities in function-calling, these advancements primarily rely on accessing the functions' responses. This methodology is practical for simpler APIs but …
Program SynthesisTrajectory PlanningPix2Code: Learning to Compose Neural Visual Concepts as Programs
The challenge in learning abstract concepts from images in an unsupervised fashion lies in the required integration of visual perception and generalizable relational reasoning. Moreover, the unsupervised nature of this t…
Program SynthesisRelational Reasoning