Using LLMs to do question-answering and understanding over tabular data is challenging. The naive approaches (dump text into prompt, text-to-SQL), oftentimes don’t work well over tabular data.
In this webinar we’re excited to feature the authors of two papers featuring advanced techniques for tabular data understanding with LLMs:
1. Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding, featuring Zilong Wang: https://arxiv.org/abs/2401.04398v1
2. Rethinking Tabular Data Understanding with Large Language Models, featuring Tianyang Liu: https://arxiv.org/abs/2312.16702
Both papers pinpoint shortcomings with naive approaches and propose novel techniques to reason about tabular data in a robust manner. Come check it out!
We’ve also implemented these as LlamaPack templates:
1. Chain-of-Table LlamaPack: https://llamahub.ai/l/llama_packs-tables-chain_of_table?from=all
2. Mix-Self-Consistency LlamaPack: https://llamahub.ai/l/llama_packs-tables-mix_self_consistency?from=all
Timeline:
00:00-22:45 – Chain-of-Table
22:45-24:23 – Short Break (skip this part)
24:23-45:00 – Rethinking Tabular Data Understanding + Mix-Self-Consistency
45:00 – Q&A
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