{"title":"QuArch: A Question-Answering Dataset for AI Agents in Computer Architecture","authors":"Shvetank Prakash;Andrew Cheng;Jason Yik;Arya Tschand;Radhika Ghosal;Ikechukwu Uchendu;Jessica Quaye;Jeffrey Ma;Shreyas Grampurohit;Sofia Giannuzzi;Arnav Balyan;Fin Amin;Aadya Pipersenia;Yash Choudhary;Ankita Nayak;Amir Yazdanbakhsh;Vijay Janapa Reddi","doi":"10.1109/LCA.2025.3541961","DOIUrl":null,"url":null,"abstract":"We introduce QuArch, a dataset of 1500 human-validated question-answer pairs designed to evaluate and enhance language models’ understanding of computer architecture. The dataset covers areas including processor design, memory systems, and performance optimization. Our analysis highlights a significant performance gap: the best closed-source model achieves 84% accuracy, while the top small open-source model reaches 72%. We observe notable struggles on QAs regarding memory systems and interconnection networks. Fine-tuning with QuArch improves small model accuracy by up to 8%, establishing a foundation for advancing AI-driven computer architecture research. The dataset and the leaderboard are accessible at <uri>https://quarch.ai/</uri>.","PeriodicalId":51248,"journal":{"name":"IEEE Computer Architecture Letters","volume":"24 1","pages":"105-108"},"PeriodicalIF":1.4000,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Computer Architecture Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10904448/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
We introduce QuArch, a dataset of 1500 human-validated question-answer pairs designed to evaluate and enhance language models’ understanding of computer architecture. The dataset covers areas including processor design, memory systems, and performance optimization. Our analysis highlights a significant performance gap: the best closed-source model achieves 84% accuracy, while the top small open-source model reaches 72%. We observe notable struggles on QAs regarding memory systems and interconnection networks. Fine-tuning with QuArch improves small model accuracy by up to 8%, establishing a foundation for advancing AI-driven computer architecture research. The dataset and the leaderboard are accessible at https://quarch.ai/.
期刊介绍:
IEEE Computer Architecture Letters is a rigorously peer-reviewed forum for publishing early, high-impact results in the areas of uni- and multiprocessor computer systems, computer architecture, microarchitecture, workload characterization, performance evaluation and simulation techniques, and power-aware computing. Submissions are welcomed on any topic in computer architecture, especially but not limited to: microprocessor and multiprocessor systems, microarchitecture and ILP processors, workload characterization, performance evaluation and simulation techniques, compiler-hardware and operating system-hardware interactions, interconnect architectures, memory and cache systems, power and thermal issues at the architecture level, I/O architectures and techniques, independent validation of previously published results, analysis of unsuccessful techniques, domain-specific processor architectures (e.g., embedded, graphics, network, etc.), real-time and high-availability architectures, reconfigurable systems.