[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-665553-105":59,"doc-detail-665553-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","evaluating-large-language-models-for-material-selection-paper","Evaluating Large Language Models for Material Selection - paper","","Evaluating Large Language Models for Material Selection investigates how large language models can support material selection in product conceptual design, focusing on alignment with expert recommendations. The study builds a dataset of expert material preferences and compares LLM outputs under different configurations, prompt strategies, and temperature settings. Divergences between LLM and expert choices are analyzed to identify influencing factors. Results indicate two failure modes and propose parallel prompting as a useful method. Findings also emphasize the need for further research to better replicate expert decision making while highlighting current limitations and future improvement potential.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/evaluating-large-language-models-for-material-selection-paper/665553/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/evaluating-large-language-models-for-material-selection-paper/665553.png","ImageObject",300,407,{"name":92,"@type":93},"asda1da","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is material selection important in conceptual design?","Question",{"text":112,"@type":113},"Material selection strongly affects functionality, aesthetics, manufacturability, and sustainability outcomes. It also influences performance, durability, cost, and lifecycle efficiency.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the study evaluate LLMs for material selection?",{"text":117,"@type":113},"It collects a dataset of expert material preferences and compares LLM performance against expert choices across design scenarios. Evaluation varies model configurations, prompt strategies, and temperature settings.",{"name":119,"@type":110,"acceptedAnswer":120},"What do the results suggest about LLM recommendations compared with experts?",{"text":121,"@type":113},"LLM recommendations can diverge significantly from human expert decisions, indicating current limitations. The study identifies two failure modes and suggests parallel prompting as a helpful prompting technique.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},665553,1793304939,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":41},3985747861852,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","arXiv :2405 .03695v1 [ cs .CL] 23 Apr 2024  \nDaniele Grandi1  \nAutodesk Research, San Francisco, CA, 94111 [daniele.grandi@autodesk.com](daniele.grandi@autodesk.com)  \nYash Patawari Jain  \nCarnegie Mellon University,  \nPittsburgh, PA 15289 [ypatawar@andrew.cmu.edu](ypatawar@andrew.cmu.edu)  \nAllin Groom  \nAutodesk Research,  \nLondon, UK [allin.groom@autodesk.com](allin.groom@autodesk.com)  \nBrandon Cramer  \nAutodesk Research,  \nBoston, MA [brandon.stewart.cramer@autodesk.com](brandon.stewart.cramer@autodesk.com)  \nChristopher McComb  \nDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15289  \n[ccm@cmu.edu](ccm@cmu.edu)  \nEvaluating Large Language Models for Material Selection  \nMaterial selection is a crucial step in conceptual design due to its significant impact on the functionality, aesthetics, manufacturability, and sustainability impact of the final product. This study investigates the use of Large Language Models (LLMs) for materialselection in the product design process and compares the performance of LLMs against expert choices for various design scenarios. By collecting a dataset of expert material preferences, the study provides a basis for evaluating how well LLMs can align with  \nexpert recommendations through prompt engineering and hyperparameter tuning. The divergence between LLM and expert recommendations is measured across different model configurations, prompt strategies, and temperature settings. This approach allows for a  \ndetailed analysis offactors influencing the LLMs’ effectiveness in recommending materials. The results from this study highlight two failure modes, and identify parallel prompting asa useful prompt-engineering method when using LLMs for material selection. The findings further suggest that, while LLMs can provide valuable assistance, their recommendations often vary significantly from those of human experts. This discrepancy underscores the  \nneed for further research into how LLMs can be better tailored to replicate expert decision making in material selection. This work contributes to the growing body of knowledge on how LLMs can be integrated into the design process, offering insights into their current limitations and potential for future improvements.  \nKeywords: Material Selection, Large Language Models, Context-Aware Design Assistance  \n1 Introduction  \nMaterial selection plays an important role in the conceptual design phase of engineering projects. The choice of materials directly impacts the overall performance, durability, and efficiency of a product. Selecting the right material ensures that the design meets its intended purpose, withstands loads, and performs optimally [1] . Material selection affects manufacturing costs, maintenance expenses, and the product’s lifecycle cost. Opting for costeffective materials without compromising quality is essential [2] . For instance, using lightweight materials can reduce transportation costs and energy consumption. In critical applications, material failure can have catastrophic consequences [3] . Proper materialselection ensures safety, reliability, and longevity. For instance, selecting materials with corrosion-resistant properties for marine structures can prevent premature deterioration. As an added complexity, there is also an emerging requirement for the sustainability of materials to be considered during selection, specifically how choices impact the environment. Designers must consider factors like recyclability, energy consumption during production, and the broader carbon footprint associated with a candidate material. Materials influence the ease of manufacturing and assembly [4] . Some materials are easier to shape, weld, or join, leading to efficient production processes. Conversely, poor material choices can complicate manufacturing and increase costs. Material aesthetics influence the product’s visual appeal and user experience. Whether it’s a sleek smartphone casing or a comfortable chair, ma","cbCaiixtZpsCcBjH","https://ap.wps.com/l/cbCaiixtZpsCcBjH","pdf",738640,12,"English","# Introduction\n## Material selection in conceptual design\n## Modeling material choice as an optimization problem\n### Multi-objective trade-offs and discreteness challenges\n## Motivation for using LLMs","[{\"question\":\"Why is material selection important in conceptual design?\",\"answer\":\"Material selection strongly affects functionality, aesthetics, manufacturability, and sustainability outcomes. It also influences performance, durability, cost, and lifecycle efficiency.\"},{\"question\":\"How does the study evaluate LLMs for material selection?\",\"answer\":\"It collects a dataset of expert material preferences and compares LLM performance against expert choices across design scenarios. Evaluation varies model configurations, prompt strategies, and temperature settings.\"},{\"question\":\"What do the results suggest about LLM recommendations compared with experts?\",\"answer\":\"LLM recommendations can diverge significantly from human expert decisions, indicating current limitations. The study identifies two failure modes and suggests parallel prompting as a helpful prompting technique.\"}]","Evaluating Large Language Models for Material Selection - paper | PDF"]