Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models
On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval
Abstract (arXiv)
The conventional use of the Retrieval-Augmented Generation (RAG) architecture has proven effective for retrieving information from diverse documents. However, challenges arise in handling complex table queries, especially within PDF documents containing intricate tabular structures.This research introduces an innovative approach to enhance the accuracy of complex table queries in RAG-based systems. Our methodology involves storing PDFs in the retrieval database and extracting tabular content separately. The extracted tables undergo a process of context enrichment, concatenating headers with corresponding values. To ensure a comprehensive understanding of the enriched data, we employ a fine-tuned version of the Llama-2-chat language model for summarisation within the RAG architecture. Furthermore, we augment the tabular data with contextual sense using the ChatGPT 3.5 API through a one-shot prompt. This enriched data is then fed into the retrieval database alongside other PDFs. Our approach aims to significantly improve the precision of complex table queries, offering a promising solution to a longstanding challenge in information retrieval.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 6 of its 9 references that are in the map. First posted on arXiv 4 Jan 2024.
Builds on
6 of 9 references.
Closest ancestors · 1
Same direction, newest first
Other references · 5
- 2017Attention Is All You Needcited by 59,932
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2019How Can We Know What Language Models Know?cited by 953
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understandingcited by 6
- 2026Web Retrieval-Aware Chunking (W-RAC) for Efficient and Cost-Effective Retrieval-Augmented Generation Systemscited by 1
- 2026Document Retrieval-Aware Chunking (D-RAC): Universal Retrieval-Aware Ingestion of Enterprise Documents via PDF Normalization and Multimodal Markdown Conversioncited by 0
- 2026Document Retrieval-Aware Chunking (D-RAC): Universal Retrieval-Aware Ingestion of Enterprise Documents via PDF Normalization and Multimodal Markdown Conversion21 Sep 2026
- 2026Web Retrieval-Aware Chunking (W-RAC) for Efficient and Cost-Effective Retrieval-Augmented Generation Systems8 Jan 2026
- 2025Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding19 Jun 2025
Similar, not cited
Close in wording, no citation either way
- 2025TabRAG: Improving Tabular Document Question Answering for Retrieval Augmented Generation via Structured Representations
- 2025Mixture-of-RAG: Integrating Text and Tables with Large Language Models
- 2025PDF Retrieval Augmented Question Answering
- 2025RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking
- 2025Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems
- 2025Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System
- 2024Knowledge in Triples for LLMs: Enhancing Table QA Accuracy with Semantic Extraction
- 2026Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG