mlsanityData to 7 Oct 2026

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

arXiv 2406.14732 ↗First posted 20 Jun 2024cs.CL cs.IRcited by 6

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

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.

Key facts. Cited by 6 papers in the map (data through 7 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.

Builds on

26 of 31 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context4 Mar
  2. 2024BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra27 Feb
  3. 2022Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge22 Oct
  4. 2022Large Language Models are few(1)-shot Table Reasoners13 Oct
  5. 2022Reasoning over Hybrid Chain for Table-and-Text Open Domain QA15 Jan

Other references · 21

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
  5. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  6. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  7. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
  8. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  9. 2020TaBERT: Pretraining for Joint Understanding of Textual and Tabular Datacited by 395
  10. 2020HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Datacited by 263
  11. 2020Open Question Answering over Tables and Textcited by 163
  12. 2021MATE: Multi-view Attention for Table Transformer Efficiencycited by 54
9 more
  1. 2021Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answeringcited by 25
  2. 2023Chain-of-Skills: A Configurable Model for Open-domain Question Answeringcited by 19
  3. 2022Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQAcited by 17
  4. 2023S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answeringcited by 17
  5. 2021Multi-Row, Multi-Span Distant Supervision For Table+Text Questioncited by 12
  6. 2022MuGER2: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answeringcited by 9
  7. 2024Exploring Hybrid Question Answering via Program-based Promptingcited by 8
  8. 2024MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answeringcited by 7
  9. 2024Denoising Table-Text Retrieval for Open-Domain Question Answeringcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

  1. 2025ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answeringcited by 10
  2. 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluationcited by 4
  3. 2025MULTITAT: Benchmarking Multilingual Table-and-Text Question Answeringcited by 2
  4. 2025RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QAcited by 1
  5. 2025Multi-granular Training Strategies for Robust Multi-hop Reasoning Over Noisy and Heterogeneous Knowledge Sourcescited by 0
  6. 2026Agentic DAG-Orchestrated Planner Framework for Multi-Modal, Multi-Hop Question Answering in Hybrid Data Lakescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022End-to-End Table Question Answering via Retrieval-Augmented Generation
  2. 2026Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering
  3. 2024Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach
  4. 2024MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge
  5. 2026The Reasoning Bottleneck in Graph-RAG: Structured Prompting and Context Compression for Multi-Hop QA
  6. 2025RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking
  7. 2026SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables
  8. 2026Evaluating Prompt Engineering Techniques for RAG in Small Language Models: A Multi-Hop QA Approach