Cover Image

LLMs for Science: Usage for Code Generation and Data Analysis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Nejjar, Mohamed, Zacharias, Luca, Stiehle, Fabian, Weber, Ingo
Format:	Preprint
Published:	2023
Subjects:	Software Engineering Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On LLM-Assisted Generation of Smart Contracts from Business Processes
by: Stiehle, Fabian, et al.
Published: (2025)

The Cost of Executing Business Processes on Next-Generation Blockchains: The Case of Algorand
by: Stiehle, Fabian, et al.
Published: (2024)

Process Channels: A New Layer for Process Enactment Based on Blockchain State Channels
by: Stiehle, Fabian, et al.
Published: (2023)

Code-Vision: Evaluating Multimodal LLMs Logic Understanding and Code Generation Capabilities
by: Wang, Hanbin, et al.
Published: (2025)

Insights from the Usage of the Ansible Lightspeed Code Completion Service
by: Sahoo, Priyam, et al.
Published: (2024)

CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation
by: Yan, Weixiang, et al.
Published: (2023)

To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation
by: Zhang, Yitong, et al.
Published: (2026)

Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets
by: Jelodar, Hamed, et al.
Published: (2025)

VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation
by: Ni, Yuansheng, et al.
Published: (2025)

Search-Based LLMs for Code Optimization
by: Gao, Shuzheng, et al.
Published: (2024)

An Architecture for Decentralised Deployment and Operation of Blockchain Applications
by: Stiehle, Fabian, et al.
Published: (2026)

Exploring Data-Efficient Adaptation of Large Language Models for Code Generation
by: Jiang, Xue, et al.
Published: (2024)

Can Coding Agents Reproduce Findings in Computational Materials Science?
by: Huang, Ziyang, et al.
Published: (2026)

A Critical Study of What Code-LLMs (Do Not) Learn
by: Anand, Abhinav, et al.
Published: (2024)

IndustryCode: A Benchmark for Industry Code Generation
by: Zeng, Puyu, et al.
Published: (2026)

Rethinking Repetition Problems of LLMs in Code Generation
by: Dong, Yihong, et al.
Published: (2025)

Risk Assessment Framework for Code LLMs via Leveraging Internal States
by: Huang, Yuheng, et al.
Published: (2025)

LeDex: Training LLMs to Better Self-Debug and Explain Code
by: Jiang, Nan, et al.
Published: (2024)

OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
by: Zheng, Tianyu, et al.
Published: (2024)

CodeMirage: Hallucinations in Code Generated by Large Language Models
by: Agarwal, Vibhor, et al.
Published: (2024)

CodeNav: Beyond tool-use to using real-world codebases with LLM agents
by: Gupta, Tanmay, et al.
Published: (2024)

EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories
by: Li, Jia, et al.
Published: (2024)

InverseCoder: Self-improving Instruction-Tuned Code LLMs with Inverse-Instruct
by: Wu, Yutong, et al.
Published: (2024)

CodeV: Issue Resolving with Visual Data
by: Zhang, Linhao, et al.
Published: (2024)

MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks
by: Chervyakov, Artem, et al.
Published: (2025)

CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment
by: Jiang, Xue, et al.
Published: (2025)

ConvCodeWorld: Benchmarking Conversational Code Generation in Reproducible Feedback Environments
by: Han, Hojae, et al.
Published: (2025)

ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models
by: Han, Hojae, et al.
Published: (2024)

Code Repair with LLMs gives an Exploration-Exploitation Tradeoff
by: Tang, Hao, et al.
Published: (2024)

AutoCode: LLMs as Problem Setters for Competitive Programming
by: Zhou, Shang, et al.
Published: (2025)

CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback
by: Sun, Qiushi, et al.
Published: (2025)

Semantically Aligned Question and Code Generation for Automated Insight Generation
by: Singha, Ananya, et al.
Published: (2024)

Top General Performance = Top Domain Performance? DomainCodeBench: A Multi-domain Code Generation Benchmark
by: Zheng, Dewu, et al.
Published: (2024)

BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
by: Zhuo, Terry Yue, et al.
Published: (2024)

Magicoder: Empowering Code Generation with OSS-Instruct
by: Wei, Yuxiang, et al.
Published: (2023)

Pragmatic Reasoning improves LLM Code Generation
by: Cao, Zhuchen, et al.
Published: (2025)

Multilingual Multimodal Software Developer for Code Generation
by: Chai, Linzheng, et al.
Published: (2025)

UA-Code-Bench: A Competitive Programming Benchmark for Evaluating LLM Code Generation in Ukrainian
by: Syromiatnikov, Mykyta, et al.
Published: (2025)

Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation
by: Lee, Seonghyeon, et al.
Published: (2024)

BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution
by: Zhuo, Terry Yue, et al.
Published: (2025)