# TTQA-RS- A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization

arXiv: https://arxiv.org/abs/2406.14732 · HTML: https://mlsanity.com/paper/2406.14732/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 26 of its 31 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 26 of 26)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [Compositional Semantic Parsing on Semi-Structured Tables](https://mlsanity.com/paper/1508.00305/) (2015)
- [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)
- [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://mlsanity.com/paper/2004.07347/) (2020)
- [Open Question Answering over Tables and Text](https://mlsanity.com/paper/2010.10439/) (2020)
- [Large Language Models are few(1)-shot Table Reasoners](https://mlsanity.com/paper/2210.06710/) (2022)
- [MATE: Multi-view Attention for Table Transformer Efficiency](https://mlsanity.com/paper/2109.04312/) (2021)
- [Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering](https://mlsanity.com/paper/2108.02866/) (2021)
- [BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra](https://mlsanity.com/paper/2402.17882/) (2024)
- [ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context](https://mlsanity.com/paper/2403.02177/) (2024)
- [Chain-of-Skills: A Configurable Model for Open-domain Question Answering](https://mlsanity.com/paper/2305.03130/) (2023)
- [Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA](https://mlsanity.com/paper/2210.05197/) (2022)
- [S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering](https://mlsanity.com/paper/2305.11725/) (2023)
- [Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge](https://mlsanity.com/paper/2210.12338/) (2022)
- [Multi-Row, Multi-Span Distant Supervision For Table+Text Question](https://mlsanity.com/paper/2112.07337/) (2021)
- [Reasoning over Hybrid Chain for Table-and-Text Open Domain QA](https://mlsanity.com/paper/2201.05880/) (2022)
- [MuGER2: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering](https://mlsanity.com/paper/2210.10350/) (2022)
- [Exploring Hybrid Question Answering via Program-based Prompting](https://mlsanity.com/paper/2402.10812/) (2024)
- [MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering](https://mlsanity.com/paper/2403.19116/) (2024)
- [Denoising Table-Text Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2403.17611/) (2024)

## Built on by (most cited first, 6 of 6)

- [ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering](https://mlsanity.com/paper/2506.00232/) (2025)
- [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)
- [MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering](https://mlsanity.com/paper/2502.17253/) (2025)
- [RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA](https://mlsanity.com/paper/2510.20505/) (2025)
- [Multi-granular Training Strategies for Robust Multi-hop Reasoning Over Noisy and Heterogeneous Knowledge Sources](https://mlsanity.com/paper/2502.05944/) (2025)
- [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)

Question answering (QA) over tables and text has gained much popularity over the years. Multi-hop table-text QA requires multiple hops between the table and text, making it a challenging QA task. Although several works have attempted to solve the table-text QA task, most involve training the models and requiring labeled data. In this paper, we have proposed a Retrieval Augmented Generation (RAG) based model - TTQA-RS: A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization. Our model uses an enhanced retriever for table-text information retrieval and uses augmented knowledge, including table-text summary with decomposed sub-questions with answers for a reasoning-based table-text QA. Using open-source language models, our model outperformed all existing prompting methods for table-text QA tasks on existing table-text QA datasets, such as HybridQA and OTT-QA's development set. Our experiments demonstrate the potential of prompt-based approaches using open-source LLMs. Additionally, by using LLaMA3-70B, our model achieved state-of-the-art performance for prompting-based methods on multi-hop table-text QA.