24 papers · ranked by Valyu relevance
Samuel Kernan Freire, Chaofan Wang, Mina Foosherian, Stefan Wellsandt + 2 more
'Stefan Wellsandt' 'Santiago Ruiz-Arenas' 'Evangelos Niforatos'] Recent advances in natural language processing enable more intelligent ways to support knowledge sharing in factories. In manufacturing, operating production lines has become increasingly knowledge-intensive, putting strain on a factory's capacity to…
Jing Wui Yeoh, C. Pawan K. Patro, Limsoon Wong, Chueh Loo Poh
Genome-scale metabolic models (GSMs) underpin pathway and strain engineering by linking genes to metabolic reactions and enabling system-level simulation of cellular fluxes and intervention effects, yet end-to-end analysis workflows remain fragmented, expert-demanding, and slow to adapt. Large language models (LLMs)…
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…
Corbo, Simone
This is a preprint of a chapter accepted for publication in Challenges and Applications of Generative Large Language Models (Pillai, A.S., Tedesco, R., Scotti, V., 2026. Challenges and Applications of Generative Large Language Models. Elsevier. ISBN: 9780443335921.). The final published version will be available from…
Bridget Dwyer, Matthew Flathers, Akane Sano, Allison Dempsey + 29 more
Individuals are increasingly utilizing large language model (LLM)-based tools for mental health guidance and crisis support in place of human experts. While AI technology has great potential to improve health outcomes, insufficient empirical evidence exists to suggest that AI technology can be deployed as a clinical…
Wenbin Guo, Minzhe Zhang, Bowei Han, Youjia Ma + 6 more
Large language model (LLM)-based agents hold transformative potential for automating bioinformatics workflows; however, systematic evaluations of their capabilities remain limited, hindering a clear assessment of their readiness for real-world application. We introduce PromptBio-Bench, a comprehensive evaluation suite…
Timothy R. McIntosh, Teo Sušnjak, Tong Liu, Paul Watters + 1 more
Artificial Intelligence Authors: ['Timothy R. McIntosh' 'Teo Sušnjak' 'Tong Liu' 'Paul Watters' 'Malka N. Halgamuge'] Abstract—The rapid rise in popularity of Large Language Models (LLMs) with emerging capabilities has spurred publi c curiosity to evaluate and compare different LLMs, leading many researchers to propose…
Wenpin Hou, Xinyi Shang, Zhicheng Ji
Large language models have demonstrated great potential in biomedical research. However, their ability to serve as a knowledge base for genomic research remains unknown. We developed GeneTuring, a comprehensive Q&A database containing 1,200 questions in genomics, and manually scored 25,200 answers provided by six GPT…
Reuben Sarwal, Victor Tarca, Claire Dubin, Nikolas Kalavros + 8 more
Generative AI, particularly large language models (LLMs), is increasingly being used in computational biology to support code generation for data analysis. In this study, we evaluated the ability of LLMs to generate functional R and Python code for predictive modeling tasks, leveraging standardized molecular datasets…
Neel Guha, Julian Nyarko, Daniel E. Ho, Christopher Ré + 36 more
'Adam Chilton' 'Aditya Narayana' 'Alex Chohlas-Wood' 'Austin Peters' 'Brandon Waldon' 'Daniel N. Rockmore' 'Diego Zambrano' 'Dmitry Talisman' 'Enam Hoque' 'Faiz Surani' 'Frank Fagan' 'Galit A. Sarfaty' 'Gregory M. Dickinson' 'Haggai Porat' 'Jason Hegland' 'Jessica J Wu' 'Joe Nudell' 'Joel Niklaus' 'John J. Nay'…
George Crowley, Stephen R. Quake
We developed an open-source package called AnnDictionary (https://github.com/ggit12/anndictionary/) to facilitate the parallel, independent analysis of multiple anndata. AnnDictionary is built on top of LangChain and AnnData and supports all common large language model (LLM) providers. AnnDictionary only requires 1…
Елена Карданова, Alina Ivanova, Ксения Тарасова, Тарас Пащенко + 6 more
Competency Benchmark for Large Language Models Authors: ['Елена Карданова' 'Alina Ivanova' 'Ксения Тарасова' 'Тарас Пащенко' 'Aleksei Tikhoniuk' 'Elen Yusupova' 'Anatoly Kasprzhak' 'Yaroslav Kuzminov' 'Ekaterina Kruchinskaia' 'Irina Brun'] The era of large language models (LLM) raises questions not only about how to…
Joshua Kelsall, Xingwei Tan, Aislinn Bergin, Jiahong Chen + 6 more
The legal profession faces mounting pressures, including case backlogs and limited access to legal services. Large language models (LLMs), such as OpenAI’s GPT series, have been touted as potential solutions, promising to streamline tasks such as legal drafting, summarisation, analysis, and advice. Proponents argue…
Pin-Er Chen, Da-Chen Lian, Shu-Kai Hsieh, Sieh-Chuen Huang + 7 more
'Hsuan-Lei Shao' 'Jun-Wei Chiu' 'Yang-Hsien Lin' 'Zih-Ching Chen' 'Cheng-Kuang' 'Eddie TC Huang' 'Simon See'] The recent advances in Legal Large Language Models (LLMs) have transformed the landscape of legal research and practice by automating tasks, enhancing research precision, and supporting complex decision-making…
Authors not listed
Artificial intelligence, represented by large language models (LLMs), has demonstrated tremendous capabilities in natural language recognition and extraction. To further evaluate the performance of various LLMs in extracting information from academic papers, this study explores the application of LLMs in reticular…
Neel Guha, Andy K. Zhang, Christine Tsang, Christopher D. Manning + 2 more
Despite substantial excitement around the use of AI in law, little information exists on the performance and associated risks of the domain’s widely marketed tools. Recent work, for instance, has demonstrated the significant potential for “hallucinations”-wherein models make up facts, law, and precedent-leading Chief…
John J. Nay, David Karamardian, Sarah B. Lawsky, Wenting Tao + 5 more
Better understanding of Large Language Models' (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence and leveraging LLMs to identify inconsistencies in law. This paper explores LLM capabilities in applying tax law. We choose this area of law…
Authors not listed
The interdisciplinary nature of redox flow batteries (RFBs), spanning chemistry, materials, and engineering, has led to a vast and fragmented body of research, hindering the efficient synthesis of knowledge. An intelligent question-answering system is there-fore essential to organize this dispersed knowledge, enhance…
Betty Tärning, Trond A. Tjøstheim, Annika Wallin
The use of Large Language Models (LLMs) such as ChatGPT is a prominent topic in higher education, prompting debate over their educational impact. Studies on the effect of LLMs on learning in higher education often rely on self-reported data, leaving an opening for complimentary methodologies. This study contributes by…
Authors not listed
Accurate benchmarks are key to assessing the accuracy and robustness of computational methods, yet most available benchmark sets focus on equilibrium geometries, limiting their utility for applications involving non-equilibrium structures such as ab initio molecular dynamics and automated reaction-path exploration. To…
Kobi Felton, Jan Rittig, Alexei Lapkin
In the fine chemicals industry, reaction screening and optimisation are essential to development of new products. However, this screening can be extremely time and labor intensive, especially when intuition is used. Machine learning offers a solution through iterative suggestions of new experiments based on past…
Authors not listed
Validating the performance of exchange-correlation functionals is vital to ensure the reliability of DFT calculations. Typically, these validations involve benchmarking datasets. Currently, such datasets are typically assembled in an unprincipled manner, suffering from uncontrolled chemical bias, and limiting the…
Authors not listed
Generative artificial intelligence (AI) tools such as large language models (LLMs) have become ubiquitous in everyday life, and are also increasingly finding applications in the chemical sciences. Although LLMs have achieved impressive performance on many chemistry tasks, optimal performance requires proper use…
Riley Hickman, Priyansh Parakh, Austin Cheng, Qianxiang Ai + 3 more
Experiment planning algorithms are a required component of autonomous platforms for scientific discovery. Selecting a suitable optimization algorithm for a novel application is an important yet difficult choice a researcher has to make based on past empirical performance on similar tasks. To facilitate the evaluation…