Extent of grounded reasoning in financial documents for investment insight

Determine the extent to which large language models can ground their reasoning in financial documents to uncover new insights for investment decision-making, assessing whether their conclusions are supported by evidence within the documents themselves.

Background

The paper motivates a knowledge discovery dimension where LLMs must derive non-trivial investment insights from long, hybrid-content financial documents (e.g., text and tables). The authors note that despite growing attention to LLMs in investment contexts, it remains unclear how well these models can ground their reasoning in actual financial documents rather than relying on unsupported internal inferences.

FinTrust includes tasks that require cross-document reasoning and numerical calculations using 10-K reports and other financial materials, intended to probe whether models can produce evidence-backed insights. The open question frames the need for a systematic determination of the degree and reliability of such grounding.