The field of financial natural language processing (NLP) and multimodal reasoning is rapidly evolving, with a focus on developing more sophisticated models that can accurately analyze and predict financial events and trends. Recent research has emphasized the importance of integrating multiple data sources and modalities, such as text, images, and audio, to improve the accuracy and robustness of financial models. One notable direction is the development of multimodal financial foundation models (MFFMs), which can digest and process multiple types of financial data, including fundamental data, market data, and alternative data. These models have the potential to revolutionize the field of financial services and investment processes by enabling a deeper understanding of the underlying complexity associated with numerous financial tasks and data.

Noteworthy papers in this area include FinRipple, which proposes a framework for aligning large language models with financial market data to predict ripple effects, and FinMME, which introduces a benchmark dataset for evaluating multimodal financial reasoning. FinChain is also a notable contribution, as it provides a symbolic benchmark for verifiable chain-of-thought financial reasoning, spanning multiple financial domains and topics. Additionally, FinMultiTime introduces a large-scale, multimodal financial time series dataset that temporally aligns four distinct modalities across the S&P 500 and HS 300 universes, enabling more accurate financial time series predictions.