20 papers · ranked by Valyu relevance
Yihong Dong, Xue Jiang, Jiaru Qian, Wang Tian + 2 more
—Code generation agents powered by large language models (LLMs) are revolutionizing the software development paradigm. Distinct from previous code generation techniques, code generation agents are characterized by three core features. 1) Autonomy: the ability to independently manage the entire workflow, from task…
Man-Fai Wong, Shangxin Guo, Ching-Nam Hang, Siu-Wai Ho + 2 more
'Chee-Wei Tan' 'Lei Wang'] This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs…
Huy Le, Phong Nguyen, Hao Do, Tuan Nguyen + 3 more
Automatic code generation has long been regarded as a critical and aspirational goal in Software Engineering research [1, 2, [3]]. Its core objective is to translate userprovided requirements—often expressed in natural language—into executable source code, thereby streamlining the software development process. This…
Naizhu Jin, Zhong Li, Tian Zhang, Qingkai Zeng
—With the rapid development of code intelligence, the application of multiple programming languages is becoming increasingly widespread. However, most existing code generation models mainly focus on a single or a few programming languages, resulting in unsatisfactory performance in a multilingual environment.…
Ekaterina Trofimova, Emil Sataev, Andrey Ustyuzhanin, Xiangjie Kong
In the ever-evolving landscape of machine learning, seamless translation of natural language descriptions into executable code remains a formidable challenge. This article introduces Linguacodus, an innovative framework designed to tackle this challenge by deploying a dynamic pipeline that iteratively transforms…
Zhao Tian, Junjie Chen
Code generation refers to the automatic generation of source code based on a given programming specification, which has garnered significant attention particularly with the advancement of large language models (LLMs). However, due to the inherent complexity of real-world problems, the LLM-generated code often fails to…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Xinyi He, Jiaru Zou, Yun Lin, Mengyu Zhou + 3 more
and Correctness Testing Authors: ['Xinyi He' 'Jiaru Zou' 'Yun Lin' 'Mengyu Zhou' 'Han Shi' 'Zejian Yuan' 'Dongmei Zhang'] Large Language Models (LLMs) have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world…
Bo Cai, Jian Luo, Zhen Feng
Graphical user interfaces (GUIs) are widely used in human-computer interaction, providing a convenient interface for operation. Automating the conversion of GUI design images into source code can significantly reduce the coding workload for front-end developers. Detecting elements in GUI images is a key challenge in…
Pavel Kodytek, Alexandra Bodzas, Jan Zidek, Govind Vashishtha
Continual technological advances associated with the recent automation revolution have tremendously increased the impact of computer technology in the industry. Software development and testing are time-consuming processes, and the current market faces a lack of specialized experts. Introducing automation to this field…
Zi Wang, Xiaoyu Zhu, Hongqiang Wang, Yichun Yu + 2 more
Formal verification ensures software correctness but faces challenges in kernel specification writing, which is labor-intensive, expertise-dependent, and limited to specific targets. For complex microkernels like seL4, these issues significantly reduce the practicality of formal methods. To address this, we propose…
Pieter Floris Jacobs, Robert Pollice
Scientists across domains are often challenged to master domain-specific languages (DSLs) for their research, which are merely a means to an end but are pervasive in fields like computational chemistry. Automated code generation promises to overcome this barrier, allowing researchers to focus on their core expertise.…
Shuai Wang, Ding Liang, Li Shen, Yong Luo + 3 more
Selective Contrastive Decoding Authors: ['Shuai Wang' 'Ding Liang' 'Li Shen' 'Yong Luo' 'Zheng He' 'Wei Yu' 'Dacheng Tao'] Large language models (LLMs) have shown remarkable capabilities in code generation. However, the effects of hallucinations (e.g., output noise) make it particularly challenging for LLMs to generate…
Jacqueline A Jansen, Artür Manukyan, Nour Al Khoury, Altuna Akalin
Data analysis is constrained by a shortage of skilled experts, particularly in biology, where detailed data interpretation is vital for understanding complex biological processes and developing new treatments and diagnostics. To address this, we developed mergen, an R package that leverages Large Language Models (LLMs)…
Huifang Ma, Zhicheng Ji
Large language models have shown remarkable capabilities in algorithm design, but their effectiveness in solving data science challenges remains poorly understood. We conducted a classroom experiment in which graduate students used large language models (LLMs) to solve biomedical data science challenges on Kaggle.…
Robert Haase, Christian Tischer, Jean-Karim Hériché, Nico Scherf
In the computational age, life-scientists often have to write Python code to solve bio-image analysis (BIA) problems. Many of them have not been formally trained in programming though. Code-generation, or coding assistance in general, with Large Language Models (LLMs) can have a clear impact on BIA. To the best of our…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Authors not listed
Accelerating computational materials science relies not only on hardware advances but also on software that increases the ease of working with the relevant abstractions. Creation and manipulation of crystal structures is a part of many routine materials science workflows. In this work, we demonstrate how fine tuning…
Ihsan Tolga Medeni, Metehan Ünal, Roberto Galizi, Bryan Bartley + 4 more
Large language models have transformed software engineering practices. However, generated artefacts are not always developer-friendly and may partially meet complex requirements. As the need to standardise, integrate, and develop tools in engineering biology increases, novel approaches are needed to create and maintain…
Jiayi Li, Litian Liang, Shiyi Du, Shijie Tang + 2 more
Codon sequence design is crucial for generating mRNA sequences with desired functional properties for tasks such as creating novel mRNA vaccines or gene editing therapies. Yet existing methods lack flexibility and controllability to adapt to various design objectives. We propose a novel framework, ARCADE, that enables…