# MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering

arXiv: https://arxiv.org/abs/2403.19116 · HTML: https://mlsanity.com/paper/2403.19116/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://mlsanity.com/paper/2212.14024/) (2022)
- [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://mlsanity.com/paper/2004.07347/) (2020)
- [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://mlsanity.com/paper/2107.07653/) (2021)
- [Open Question Answering over Tables and Text](https://mlsanity.com/paper/2010.10439/) (2020)
- [A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization](https://mlsanity.com/paper/1902.01069/) (2019)
- [PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training](https://mlsanity.com/paper/2211.02816/) (2022)
- [Table-based Fact Verification with Self-adaptive Mixture of Experts](https://mlsanity.com/paper/2204.08753/) (2022)

## Built on by (most cited first, 7 of 7)

- [TTQA-RS- A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization](https://mlsanity.com/paper/2406.14732/) (2024)
- [Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation](https://mlsanity.com/paper/2510.09671/) (2025)
- [Reasoning Factual Knowledge in Structured Data with Large Language Models](https://mlsanity.com/paper/2408.12188/) (2024)
- [Off-the-shelf ChatGPT is a Good Few-shot Human Motion Predictor](https://mlsanity.com/paper/2405.15267/) (2024)
- [Team Anotheroption at SemEval-2025 Task 8: Bridging the Gap Between Open-Source and Proprietary LLMs in Table QA](https://mlsanity.com/paper/2506.09657/) (2025)
- [Replacing Multi-Step Assembly of Data Preparation Pipelines with One-Step LLM Pipeline Generation for Table QA](https://mlsanity.com/paper/2602.22721/) (2026)
- [Agentic DAG-Orchestrated Planner Framework for Multi-Modal, Multi-Hop Question Answering in Hybrid Data Lakes](https://mlsanity.com/paper/2603.14229/) (2026)

## Abstract (arXiv)

In today's fast-paced industry, professionals face the challenge of summarizing a large number of documents and extracting vital information from them on a daily basis. These metrics are frequently hidden away in tables and/or their nested hyperlinks. To address this challenge, the approach of Table Question Answering (QA) has been developed to extract the relevant information. However, traditional Table QA training tasks that provide a table and an answer(s) from a gold cell coordinate(s) for a question may not always ensure extracting the accurate answer(s). Recent advancements in Large Language Models (LLMs) have opened up new possibilities for extracting information from tabular data using prompts. In this paper, we introduce the Multi-hop Few-shot Open Rich Table QA (MFORT-QA) approach, which consists of two major steps. The first step involves Few-Shot Learning (FSL), where relevant tables and associated contexts of hyperlinks are retrieved based on a given question. The retrieved content is then used to construct few-shot prompts as inputs to an LLM, such as ChatGPT. To tackle the challenge of answering complex questions, the second step leverages Chain-of-thought (CoT) prompting to decompose the complex question into a sequential chain of questions and reasoning thoughts in a multi-hop manner. Retrieval-Augmented Generation (RAG) enhances this process by retrieving relevant tables and contexts of hyperlinks that are relevant to the resulting reasoning thoughts and questions. These additional contexts are then used to supplement the prompt used in the first step, resulting in more accurate answers from an LLM. Empirical results from OTT-QA demonstrate that our abstractive QA approach significantly improves the accuracy of extractive Table QA methods.