[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-393519-105":53,"doc-detail-393519-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","realfin-how-well-do-llms-reason-about-finance-when-users-leave-things-unsaid","RealFin - How Well Do LLMs Reason About Finance When Users Leave Things Unsaid?","","Reliable financial reasoning requires knowing not only how to answer, but also when an answer cannot be justified. In real financial practice, problems often rely on implicit assumptions that are taken for granted rather than stated explicitly, causing underdetermined questions to look solvable. REALFIN is a bilingual benchmark that evaluates financial reasoning by systematically removing essential premises from exam-style questions, yielding tasks for answering, detecting missing information, and rejecting unjustified options.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/realfin-how-well-do-llms-reason-about-finance-when-users-leave-things-unsaid/393519/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/realfin-how-well-do-llms-reason-about-finance-when-users-leave-things-unsaid/393519.png","ImageObject",442,249,{"name":88,"@type":89},"Pentious","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-16","2026-09-26",true,{"@type":98,"interactionType":99,"userInteractionCount":73},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What problem does REALFIN target in financial reasoning with LLMs?","Question",{"text":108,"@type":109},"It targets the ability to recognize when available information is insufficient to justify a determinate conclusion, not only correct answering under well-posed conditions.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How does REALFIN construct condition-missing questions?",{"text":113,"@type":109},"It starts from professional exam-style questions and creates paired counterparts by removing logically necessary premises while keeping the questions linguistically plausible.",{"name":115,"@type":106,"acceptedAnswer":116},"What task formulations does the paper evaluate using REALFIN?",{"text":117,"@type":109},"It evaluates models under three formulations: answering correctly, recognizing missing information, and rejecting unjustified options.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},393519,1792168125,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":73,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":8},1374404730887,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","RealFin: How Well Do LLMs Reason About Finance When Users Leave Things Unsaid?  \nYuyang Dai1 * , Yan Lin1,2 * , Zhuohan Xie3 , Yuxia Wang1  \n1INSAIT, Sofia University “St. Kliment Ohridski”  \n2Newcastle University 3MBZUAI  \n[y9657422@gmail.com](y9657422@gmail.com) , [y.lin64@ncl.ac.uk](y.lin64@ncl.ac.uk)  \n[zhuohan.xie@mbzuai.ac.ae](zhuohan.xie@mbzuai.ac.ae) , [yuxia.wang@insait.ai](yuxia.wang@insait.ai)  \narXiv :2602 .07096v2 [ q-fin . ST] 26 Apr 2026  \nAbstract  \nReliable financial reasoning requires knowing not only how to answer, but also when an answer cannot be justified. In real financial practice, problems often rely on implicit assumptions that are taken for granted rather than stated explicitly, causing problems to appear solvable while lacking enough information for a definite answer. We introduce REALFIN, a bilingual benchmark that evaluates financial reasoning by systematically removing essential premises from exam-style questions while keeping them linguistically plausible. Based on this, we evaluate models under three formulations that test answering, recognizing missing information, and rejecting unjustified options, and find consistent performance drops when key conditions are absent. Generalpurpose models tend to over-commit and guess, while most finance-specialized models fail to clearly identify missing premises. These results highlight a critical gap in current evaluations and show that reliable financial models must know when a question should not be answered. The dataset and code are available at [https://github.com/insait-institute/RealFin](https://github.com/insait-institute/RealFin).  \n1 Introduction  \nA central requirement of reliable reasoning in highstakes domains is not only answering correctly when a task is well-posed, but also recognizing when the available information does not justify a determinate conclusion. In real-world financial problems, key premises needed for decision-making, such as assumptions, constraints, time horizons, or applicable regulatory and accounting standards, are often missing or unspecified in users’ initial requests (Ferson, 2025) . When this happens, the difficulty is not computation, but assessing whether any conditions are missing to forward a valid reasoning process. This situation is common in complex financial practice and professional training, where  \n*These authors contributed equally to this work.  \nproblems are rarely well defined from beginning. They appear solvable but are logically underdetermined (Diebold and Yılmaz, 2015) . In such cases, committing to a specific assumption is unjustified, since multiple interpretations lead to different conclusions. A rational professional response is to withhold commitment until the missing conditions are clarified (Ferson, 2025) .  \nDespite its practical importance, examination of such capabilities is largely absent in current financial evaluations. Most financial benchmarks adopt an implicit closed-world assumption that every question is fully specified and admits a single correct answer (Jiang et al., 2025) . Consequently, benchmark design focuses on whether a model can select the correct option given complete premises, while excluding cases where missing conditions invalidate the act of answering, or no correct answers among the given options. Systems such as FinEval, FinBen, FinMaster, and the Open FinLLM Leaderboard follow this paradigm, limiting their ability to assess whether models can identify under-specified questions or confidently answer unsolvable ones (Islam et al., 2023 ; Wu et al., 2025) . While abstention and unanswerable question detection have been studied in general domains (Wenet al., 2025 ; Kirichenko et al., 2025), their intersection with professional financial reasoning remains largely unexplored.  \nTo address this gap, we introduce REALFIN, a bilingual financial reasoning benchmark that explicitly evaluates models with incomplete problems. Starting from professional, well-posed exam-style que","cbCaivpQWNCLKQzM","https://ap.wps.com/l/cbCaivpQWNCLKQzM","pdf",945758,31,"English","# Introduction\n## The gap in current financial evaluations\n## REALFIN benchmark design\n## Task formulations and examples","[{\"question\":\"What problem does REALFIN target in financial reasoning with LLMs?\",\"answer\":\"It targets the ability to recognize when available information is insufficient to justify a determinate conclusion, not only correct answering under well-posed conditions.\"},{\"question\":\"How does REALFIN construct condition-missing questions?\",\"answer\":\"It starts from professional exam-style questions and creates paired counterparts by removing logically necessary premises while keeping the questions linguistically plausible.\"},{\"question\":\"What task formulations does the paper evaluate using REALFIN?\",\"answer\":\"It evaluates models under three formulations: answering correctly, recognizing missing information, and rejecting unjustified options.\"}]","RealFin - How Well Do LLMs Reason About Finance When Users Leave Things Unsaid? | PDF",1790389033]