Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
We collect papers about "large language models (LLM) for table-related tasks", e.g., using LLM for Table QA task. “表格+LLM”相关论文整理
| Date | Stars |
|---|---|
| 2026-07-31 | 633 |
| 2026-08-06 | 633 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# 🤝 Contributions Welcome 🚀 Due to limited time, I cannot keep track of every new paper very promptly, please feel free to submit a **Pull Request** to add your papers or submit **Issues** to remind me, I will add them ASAP. Let's maintain this paper list collaboratively. 🤝 # A-Paper-List-of-Awesome-Tabular-LLMs Different types of tables are widely used to store and present information. To automatically process numerous tables and gain valuable insights, researchers have proposed a series of deep-learning models for various table-based tasks, e.g., table question answering (TQA), table-to-text (T2T), text-to-sql (NL2SQL) and table fact verification (TFV). Recently, the emerging [Large Language Models (LLMs)](https://github.com/Hannibal046/Awesome-LLM#chatgpt-evaluation) and more powerful [Multimodal Large Language Models (MLLMs)](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) have opened up new possibilities for processing the tabular data, i.e., we can use one general model to process diverse tables and fulfill different tabular tasks based on the user natural language instructions. We refer to these LLMs speciallized for tabular tasks as `Tabular LLMs`. **In this repository, we collect a paper list about recent Tabular (M)LLMs and divide them into the following categories based on their key idea.** --- <font size=8><center><b> Table of Contents: </b> </center></font> 1. [**Survey of Tabular LLMs and table understanding**](#1-survey-of-tabular-llms-and-table-understanding) 2. [**Prompting LLMs for different tabular tasks**](#2-prompting-llms-for-different-tabular-tasks), e.g., in-context learning, prompt engineering and integrating external tools. 3. [**Training LLMs for better table understanding ability**](#3-training-llms-for-better-table-understanding-ability), e.g., training existing LLMs by instruction fine-tuning or post-pretraining. 4. [**Developing Agents for tabular data**](#4-developing-agents-for-understanding-and-processing-tabular-data), e.g., devolping copilot for processing excel tables. 5. [**RAG with tabular data**](#5-rag-with-tabular-data), e.g., devolping RAG systems for understanding long tables. 6. [**Empirical study for evaluating LLMs' table understanding ability**](#6-empirical-study-for-evaluating-llms-table-understanding-ability), e.g., exploring the influence of various table types or table formats. 7. [**Multimodal table understanding**](#7-multimodal-table-understanding), e.g., training MLLMs to understand diverse table images and textual user requests. 8. [**Table Understanding datasets and benchmarks**](#8-table-understanding-datasets-and-benchmarks), e.g., valuable datasets and benchmarks for model training and evaluation. 9. [**Evaluation Metrics for Table Understanding**](#9-designing-evaluation-metrics-for-table-understanding), e.g., devising better evaluation method for table understanding. --- <font size=8><center><b> Task Names and Abbreviations: </b> </center></font> | Task Names | Abbreviations | Task Descriptions | | :---: | :---: | :---: | | Table Question Answering | TQA | Answering questions based on the table(s), e.g., answer look-up or computation questions about table(s). | | Table-to-Text | Table2Text or T2T | Generate a text based on the table(s), e.g., generate a analysis report given a financial statement. | | Text-to-Table | Text2Table | Generate structured tables based on input text, e.g., generate a statistical table based on the game summary. | | Table Fact Verification | TFV | Judging if a statement is true or false (or not enough evidence) based on the table(s) | | Text-to-SQL | NL2SQL | Generate a SQL statement to answer the user question based on the database schema | | Tabular Mathematical Reasoning | TMR | Solving mathematical reasoning problems based on the table(s), e.g., solve math word problems related to a table | | Table-and-Text Question Answering | TAT-QA | Answering questions based on both table(s) and their related texts
Excerpt of 49,744 characters
Read on GitHub
Mingyu Zheng · UCAS · China
51
2
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:06525ccb897adca3, llm:Repository description: 'We collect papers about "large language models (LLM) for table-related tasks", e.g., using LLM for Table QA task. “表格+LLM”相关论文整理' (collection of papers).
matched fp:06525ccb897adca3, llm:Repository description: 'We collect papers about "large language models (LLM) for table-related tasks", e.g., using LLM for Table QA task. “表格+LLM”相关论文整理' (collection of papers).
matched fp:06525ccb897adca3, llm:Repository description: 'We collect papers about "large language models (LLM) for table-related tasks", e.g., using LLM for Table QA task. “表格+LLM”相关论文整理' (collection of papers).