Beyond Extraction: Contextualising Tabular Data for Efficient
Summarisation by Language Models
- URL: http://arxiv.org/abs/2401.02333v3
- Date: Sat, 10 Feb 2024 12:35:22 GMT
- Title: Beyond Extraction: Contextualising Tabular Data for Efficient
Summarisation by Language Models
- Authors: Uday Allu, Biddwan Ahmed, Vishesh Tripathi
- Abstract summary: The conventional use of the Retrieval-Augmented Generation architecture has proven effective for retrieving information from diverse documents.
This research introduces an innovative approach to enhance the accuracy of complex table queries in RAG-based systems.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Generative Retrieval for Book search [106.67655212825025]
We propose an effective Generative retrieval framework for Book Search.
It features two main components: data augmentation and outline-oriented book encoding.
Experiments on a proprietary Baidu dataset demonstrate that GBS outperforms strong baselines.
arXiv Detail & Related papers (2025-01-19T12:57:13Z) - Reasoning-Aware Query-Focused Summarization over Multi-Table Data [1.325953054381901]
We propose QueryTableSummarizer++, an end-to-end generative framework leveraging large language models (LLMs)
Our method eliminates the need for intermediate serialization steps and directly generates query-relevant summaries.
Experiments on a benchmark dataset demonstrate that QueryTableSummarizer++ significantly outperforms state-of-the-art baselines in terms of BLEU, ROUGE, and F1-score.
arXiv Detail & Related papers (2024-12-12T06:04:31Z) - ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation [26.4086456393314]
Long-form text generation requires coherent, comprehensive responses that address complex queries with both breadth and depth.
Existing iterative retrieval-augmented generation approaches often struggle to delve deeply into each facet of complex queries.
This paper introduces ConTReGen, a novel framework that employs a context-driven, tree-structured retrieval approach.
arXiv Detail & Related papers (2024-10-20T21:17:05Z) - TableRAG: Million-Token Table Understanding with Language Models [53.039560091592215]
TableRAG is a Retrieval-Augmented Generation (RAG) framework specifically designed for LM-based table understanding.
TableRAG leverages query expansion combined with schema and cell retrieval to pinpoint crucial information before providing it to the LMs.
Our results demonstrate that TableRAG achieves the highest retrieval quality, leading to the new state-of-the-art performance on large-scale table understanding.
arXiv Detail & Related papers (2024-10-07T04:15:02Z) - Knowledge in Triples for LLMs: Enhancing Table QA Accuracy with Semantic Extraction [1.0968343822308813]
This paper proposes a novel approach that extracts triples straightforward from tabular data and integrates it with a retrieval-augmented generation (RAG) model to enhance the accuracy, coherence, and contextual richness of responses generated by a fine-tuned GPT-3.5-turbo-0125 model.
Our approach significantly outperforms existing baselines on the FeTaQA dataset, particularly excelling in Sacre-BLEU and ROUGE metrics.
arXiv Detail & Related papers (2024-09-21T16:46:15Z) - Database-Augmented Query Representation for Information Retrieval [59.57065228857247]
We present a novel retrieval framework called Database-Augmented Query representation (DAQu)
DAQu augments the original query with various (query-related) metadata across multiple tables.
We validate DAQu in diverse retrieval scenarios that can incorporate metadata from the relational database.
arXiv Detail & Related papers (2024-06-23T05:02:21Z) - QFMTS: Generating Query-Focused Summaries over Multi-Table Inputs [63.98556480088152]
Table summarization is a crucial task aimed at condensing information into concise and comprehensible textual summaries.
We propose a novel method to address these limitations by introducing query-focused multi-table summarization.
Our approach, which comprises a table serialization module, a summarization controller, and a large language model, generates query-dependent table summaries tailored to users' information needs.
arXiv Detail & Related papers (2024-05-08T15:05:55Z) - Decomposing Complex Queries for Tip-of-the-tongue Retrieval [72.07449449115167]
Complex queries describe content elements (e.g., book characters or events), information beyond the document text.
This retrieval setting, called tip of the tongue (TOT), is especially challenging for models reliant on lexical and semantic overlap between query and document text.
We introduce a simple yet effective framework for handling such complex queries by decomposing the query into individual clues, routing those as sub-queries to specialized retrievers, and ensembling the results.
arXiv Detail & Related papers (2023-05-24T11:43:40Z) - Mixed-modality Representation Learning and Pre-training for Joint
Table-and-Text Retrieval in OpenQA [85.17249272519626]
An optimized OpenQA Table-Text Retriever (OTTeR) is proposed.
We conduct retrieval-centric mixed-modality synthetic pre-training.
OTTeR substantially improves the performance of table-and-text retrieval on the OTT-QA dataset.
arXiv Detail & Related papers (2022-10-11T07:04:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.