TableRAG: Million-Token Table Understanding with Language Models

• URL: http://arxiv.org/abs/2410.04739v1
• Date: Mon, 7 Oct 2024 04:15:02 GMT
• Title: TableRAG: Million-Token Table Understanding with Language Models
• Authors: Si-An Chen, Lesly Miculicich, Julian Martin Eisenschlos, Zifeng Wang, Zilong Wang, Yanfei Chen, Yasuhisa Fujii, Hsuan-Tien Lin, Chen-Yu Lee, Tomas Pfister,
• Abstract summary: 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.
• Score: 53.039560091592215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in language models (LMs) have notably enhanced their ability to reason with tabular data, primarily through program-aided mechanisms that manipulate and analyze tables. However, these methods often require the entire table as input, leading to scalability challenges due to the positional bias or context length constraints. In response to these challenges, we introduce TableRAG, 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. This enables more efficient data encoding and precise retrieval, significantly reducing prompt lengths and mitigating information loss. We have developed two new million-token benchmarks from the Arcade and BIRD-SQL datasets to thoroughly evaluate TableRAG's effectiveness at scale. Our results demonstrate that TableRAG's retrieval design achieves the highest retrieval quality, leading to the new state-of-the-art performance on large-scale table understanding.

Related papers

• 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)
• TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning [61.14586098005874]
  Current Large Language Models (LLMs) exhibit limited ability to understand table structures and to apply precise numerical reasoning. We introduce our Tool-Augmented Reasoning framework for Tables (TART), which integrates LLMs with specialized tools. TART contains three key components: a table formatter to ensure accurate data representation, a tool maker to develop specific computational tools, and an explanation generator to maintain explainability.
  arXiv  Detail & Related papers  (2024-09-18T06:19:59Z)
• ALTER: Augmentation for Large-Table-Based Reasoning [5.164923314261229]
  ALTER(Augmentation for Large-Table-Based Reasoning) is a framework designed to harness the latent augmentation potential in both free-form natural language (NL) questions. By utilizing only a small subset of relevant data from the table, ALTER achieves outstanding performance on table-based reasoning benchmarks.
  arXiv  Detail & Related papers  (2024-07-03T12:34:45Z)
• TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools [51.576974932743596]
  Large Language Models (LLMs) often do not perform well on queries that require the aggregation of information across texts. TACT contains challenging instructions that demand stitching information scattered across one or more texts. We construct this dataset by leveraging an existing dataset of texts and their associated tables. We demonstrate that all contemporary LLMs perform poorly on this dataset, achieving an accuracy below 38%.
  arXiv  Detail & Related papers  (2024-06-05T20:32:56Z)
• TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning [55.33939289989238]
  We propose TAP4LLM as a versatile pre-processor suite for leveraging large language models (LLMs) in table-based tasks effectively. It covers several distinct components: (1) table sampling to decompose large tables into manageable sub-tables based on query semantics, (2) table augmentation to enhance tables with additional knowledge from external sources or models, and (3) table packing & serialization to convert tables into various formats suitable for LLMs' understanding.
  arXiv  Detail & Related papers  (2023-12-14T15:37:04Z)
• HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation [7.69801337810352]
  We conduct parameter-efficient fine-tuning on the LLaMA2 model. Our approach involves injecting reasoning information into the input by emphasizing table-specific row data. On both the FetaQA and QTSumm datasets, our approach achieved state-of-the-art results.
  arXiv  Detail & Related papers  (2023-11-15T12:02:52Z)
• TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT [19.57099486334867]
  TableGPT is a framework that enables large language models (LLMs) to understand and operate on tables using external functional commands. TableGPT aims to provide convenience and accessibility to users by empowering them to effortlessly leverage tabular data.
  arXiv  Detail & Related papers  (2023-07-17T17:36:09Z)
• QTSumm: Query-Focused Summarization over Tabular Data [58.62152746690958]
  People primarily consult tables to conduct data analysis or answer specific questions. We define a new query-focused table summarization task, where text generation models have to perform human-like reasoning. We introduce a new benchmark named QTSumm for this task, which contains 7,111 human-annotated query-summary pairs over 2,934 tables.
  arXiv  Detail & Related papers  (2023-05-23T17:43:51Z)
• GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing [117.98107557103877]
  We present GraPPa, an effective pre-training approach for table semantic parsing. We construct synthetic question-pairs over high-free tables via a synchronous context-free grammar. To maintain the model's ability to represent real-world data, we also include masked language modeling.
  arXiv  Detail & Related papers  (2020-09-29T08:17:58Z)

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.