This was exactly my thought. Rather than feed the table directly to the LLM, build agents that extract the data and have the LLM act on the extracted data items. Then it’s a preference issue.
The author didn’t see much more than 60% accuracy which is not very useful for many (most?) real world tasks.
“Agents that extract the data” Are we really reinventing data frame readers to have an LLM in the critical path?
Reinventing? No. Using? Yes, for a lot of good reasons.
LLMs are expensive. Spending tokens to do something in bulk that is well suited to existing tools and algorithms, is wasteful and slow. And the main reason is that, using LLMs, the original author indicated only a 60% success rate for the task. Why spend many times more time and money and energy just to use an LLM on a well-understood preparatory task that it sucks at, when you can get much better results more inexpensively with off-the-shelf tools, and feed their results to the LLM for its unique value.