Better Think with Tables: Leveraging Tables to Enhance Large Language Model Comprehension

• URL: http://arxiv.org/abs/2412.17189v1
• Date: Sun, 22 Dec 2024 23:31:03 GMT
• Title: Better Think with Tables: Leveraging Tables to Enhance Large Language Model Comprehension
• Authors: Jio Oh, Geon Heo, Seungjun Oh, Jindong Wang, Xing Xie, Steven Euijong Whang,
• Abstract summary: Thinking with tables is a technique that assists Large Langauge Models (LLMs) to leverage tables for intermediate thinking aligning with human cognitive behavior. We show that our approach achieves a 40.29% average relative performance increase, higher robustness, and show generalizability to different requests, conditions, or scenarios.
• Score: 33.32086403802351
• License:
• Abstract: Despite the recent advancement of Large Langauge Models (LLMs), they struggle with complex queries often involving multiple conditions, common in real-world scenarios. We propose Thinking with Tables, a technique that assists LLMs to leverage tables for intermediate thinking aligning with human cognitive behavior. By introducing a pre-instruction that triggers an LLM to organize information in tables, our approach achieves a 40.29\% average relative performance increase, higher robustness, and show generalizability to different requests, conditions, or scenarios. We additionally show the influence of data structuredness for the model by comparing results from four distinct structuring levels that we introduce.

Related papers

• Towards Better Understanding Table Instruction Tuning: Decoupling the Effects from Data versus Models [62.47618742274461]
  We fine-tune base models from the Mistral, OLMo, and Phi families on existing public training datasets. Our replication achieves performance on par with or surpassing existing table LLMs. We decouple the contributions of training data and the base model, providing insight into their individual impacts.
  arXiv  Detail & Related papers  (2025-01-24T18:50:26Z)
• Rethinking Table Instruction Tuning [29.139828718538418]
  We evaluate abilities in existing table LLMs and reveal significant declines in both out-of-domain table understanding and general capabilities. We introduce TAMA, a TAble LLM instruction-tuned from LLaMA 3.1 8B Instruct, which achieves performance on par with, or surpassing GPT-3.5 and GPT-4 on table tasks.
  arXiv  Detail & Related papers  (2025-01-24T18:06:07Z)
• Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding [42.841205217768106]
  "Tree-of-Table" is a novel approach designed to enhance LLMs' reasoning capabilities over large and complex tables. We show that Tree-of-Table sets a new benchmark with superior performance, showcasing remarkable efficiency and generalization capabilities in large-scale table reasoning.
  arXiv  Detail & Related papers  (2024-11-13T11:02:04Z)
• Matchmaker: Self-Improving Large Language Model Programs for Schema Matching [60.23571456538149]
  We propose a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker self-improves in a zero-shot manner without the need for labeled demonstrations. Empirically, we demonstrate on real-world medical schema matching benchmarks that Matchmaker outperforms previous ML-based approaches.
  arXiv  Detail & Related papers  (2024-10-31T16:34:03Z)
• 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)
• 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)
• TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios [51.66718740300016]
  TableLLM is a robust large language model (LLM) with 8 billion parameters. TableLLM is purpose-built for proficiently handling data manipulation tasks. We have released the model checkpoint, source code, benchmarks, and a web application for user interaction.
  arXiv  Detail & Related papers  (2024-03-28T11:21:12Z)
• 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)
• 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.