Many experts in the field consider the integration of visual inputs, such as images along with text and speech, into large language models (LLMs) to be an important new direction in ai research. By augmenting these models to handle multiple modes of data beyond language, there is the potential to significantly expand the scope of applications they can be used for, as well as improve their overall intelligence and performance on existing NLP tasks.
The promise of multimodal ai ranges from more engaging user experiences, such as conversational agents that can see their environment and refer to objects around them, to robots that can fluidly translate commands into physical actions using combined knowledge of language and vision. . By uniting historically separate areas of ai around a unified model architecture, multimodality can accelerate progress on tasks that rely on multiple skills, such as visually answering questions or captioning images. Synergies between learning algorithms, data types, and model designs across fields could lead to rapid advancement.
Many companies have already adopted multimodality in various ways: OpenAI, ai” rel=”noopener ugc nofollow” target=”_blank”>anthropicGoogle (Bard and Gemini) allow you to upload your own image or text data and chat with them.
In this article, I hope to demonstrate a simple yet powerful application of large language models with computer vision in finance. Equity researchers and investment banking analysts may find this especially useful, as they probably spend considerable time reading reports and statements that contain multiple tables and charts. Reading extensive tables and graphs and interpreting them correctly requires a great deal of time, subject knowledge, and adequate concentration to avoid errors. What's more tedious is that analysts sometimes need to manually enter tabular data from PDF files simply to create new charts. An automated solution could alleviate these problems by extracting and interpreting key information without human oversight or fatigue.
In fact, by combining NLP with computer vision, we can create an assistant to handle many repetitive analytical tasks, freeing analysts to focus on higher levels…
