Financial assistants often answer wrong questions with right numbers
Right Numbers, Wrong Questions: Evaluating LLMs on Underspecified Financial Queries
Artificial IntelligenceComputation and Language
Summary
Financial assistants sometimes give a number that looks correct but actually answers a different question than the user intended. The authors show this happens when questions leave out important details, like which financial product or tax rule applies. They studied Indian mutual funds where details are well defined, finding that models often pick default or outdated assumptions without realizing it. This means even answers that match data can be wrong for the user’s situation. The authors offer ways to detect and control these mistakes.
What this means in practice
- •For financial technology developers: Improve financial assistants by detecting ambiguous queries and ensuring answers match the user’s actual context and product variant.$Commercial implications: Enables creation of smarter financial chatbots and advisory tools that reduce misinterpretation risks for end users.
- •For ai quality assurance teams: Develop tests that reveal when AI models silently choose alternate interpretations of financial data, improving model evaluation.
Authors
Radha Yadav, Prasanna Devadiga, Peeyush Mishra, Apoorv Singh
Abstract
Financial assistants answer questions that sound factual but depend on what the user leaves unsaid: what a data figure measures, which product variant is meant, and the investor's situation and the rules that apply. An assistant that ignores these can still answer with a real number from its data, one that answers a different question. We call this frame collapse. We study it in Indian mutual funds, where product variants and tax rules are formally defined, so the correct answer can be computed for every reading. One study tests each challenge. Given an ambiguously labelled figure, models used it, often under the user's label. Given every alternative labelled, they listed them all, but the layout decided which came first. Missing a fact about the investor, weaker agents assumed a default or an outdated tax rule; requiring the fact did not guarantee its use. Because such answers match their data, checks against the data pass them. An AI judge caught mislabelled figures only when told what each field means, and still passed answers that silently chose another reading. We show how these failures arise, not how often, and propose controls for financial assistants.