LLM-based Agents for Tabular Data Processing - Softlandia
Language models are pretty great at understanding and generating text that sounds like a human wrote it. But what about when we throw tables full of data at them? This blog post dives into how language models handle structured, tabular data, exploring the unique challenges they face and the cool benefits they offer. We’ll look into practical ways these AI tools can help us make sense of numbers and tables, using non-trivial tabular data with free text columns as an example. A component for understanding tabular data is an integral part of a solid AI engine after all - and you'll want one if you plan to fully utilize generative AI.
Understanding and acting on tabular data, such as Excel sheets and comma separated values, remains challenging for large language models (LLMs). Two technical hurdles remain
Both of the above issues are prevalent with tabular data, which often comprises rows containing numerical and textual data, in large amounts. Hence just pasting your Excel sheet to a chatbot will likely have limited utility. The results will not properly consider all the data and any numerical operations will be unreliable.
