In this video we dive into Table-GPT, a large language model (LLM) that can understand tables. Table-GPT was presented in a new research paper from Microsoft, titled "Table-GPT: Table-tuned GPT for Diverse Table Tasks". 
We first review the capabilities of current LLMs, ChatGPT in particular, to understand tables, which shows a big room for improvement.
Then, we introduce table-tuning, a concept introduced in the paper to improve the performance of LLMs on tables related tasks. This method was inspired from instruction-tuning, and so we first to a quick recap of large language models training process.
Afterwards, we dive into the creation of the dataset used for table-tuning, which is created using a method the researchers call synthesis-then-augment, which include automatic generation of the dataset from real tables, using synthesis and augmentation.
Finally, we review the results of Table-GPT comparing to GPT 3.5 and ChatGPT.

Paper page - https://arxiv.org/abs/2310.09263

Read our post - https://aipapersacademy.com/table-gpt/

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Chapters:
0:00 Introduction
0:54 Can Current LLMs Understand Tables?
3:02 Table-Tuning
5:00 Synthesis-then-Augment
8:10 Results

AI Papers Academy

Microsoft researchers introduce Table-GPT, a model fine-tuned specifically to handle tabular data tasks where standard LLMs like ChatGPT underperform. The core issue is that LLMs pre-trained on text and code struggle with two-dimensional table reasoning, especially vertical column-based queries. ChatGPT achieves only 42.2% accuracy on column identification and 51.2% on table question answering. Table-GPT addresses this via 'table-tuning', an additional fine-tuning step using a large synthesized dataset of (instruction, table, response) triplets. The dataset is built from 2.9M Wikipedia tables and 188K database tables using a synthesis-then-augment pipeline that includes instruction paraphrasing, row/column reordering, and LLM-generated reasoning labels. Results show significant performance gains over both GPT-3.5 and ChatGPT across 8 task types, including tasks the model was never trained on.

Table-GPT by Microsoft: Empower LLMs To Understand Tables