mlsanityData to 14 Oct 2026

Talking with Tables for Better LLM Factual Data Interactions

arXiv 2412.17189 ↗First posted 22 Dec 2024cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Large Language Models (LLMs) often struggle with requests related to information retrieval and data manipulation that frequently arise in real-world scenarios under multiple conditions. In this paper, we demonstrate that leveraging tabular structures in LLM interactions, is more effective than utilizing other structures for handling prevalent requests that operate over factual data. Through comprehensive evaluations across various scenarios and request types, we show that providing tabular structures yields a 40.29% average performance gain along with better robustness and token efficiency. Through attention-value analysis, we discover that tables help LLMs better locate relevant information, explaining these improvements. Beyond tables and text, we evaluate whether (1) blending structuredness within text, such as providing templates or fixing the order of attributes, and (2) other representative structures, such as knowledge graphs and JSON are helpful. We observe that utilizing tables offers the best balance between efficiency and effectiveness. The method remains robust to task complexity and adapts to unstructured sources through text-to-table conversion. Overall, we highlight the untapped potential of tabular representations for future LLM applications.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 31 of its 49 references that are in the map. First posted on arXiv 22 Dec 2024.

Builds on

31 of 49 references.

References · 31

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  4. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  5. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
  6. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  7. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  8. 2023LIMA: Less Is More for Alignmentcited by 1,100
  9. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 817
  10. 2023How Language Model Hallucinations Can Snowballcited by 311
  11. 2024Long-context LLMs Struggle with Long In-context Learningcited by 286
  12. 2024Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understandingcited by 188
19 more
  1. 2023Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?cited by 168
  2. 2020Answering Complex Open-Domain Questions with Multi-Hop Dense Retrievalcited by 162
  3. 2024Does Prompt Formatting Have Any Impact on LLM Performance?cited by 141
  4. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 120
  5. 2023Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakescited by 111
  6. 2020Logical Natural Language Generation from Open-Domain Tablescited by 90
  7. 2024What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineeringcited by 90
  8. 2024TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarioscited by 77
  9. 2022Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answeringcited by 72
  10. 2023Can Large Language Models Understand Real-World Complex Instructions?cited by 69
  11. 2024CFBench: A Comprehensive Constraints-Following Benchmark for LLMscited by 67
  12. 2020HittER: Hierarchical Transformers for Knowledge Graph Embeddingscited by 54
  13. 2021Text-to-Table: A New Way of Information Extractioncited by 47
  14. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  15. 2023Tab-CoT: Zero-shot Tabular Chain of Thoughtcited by 42
  16. 2023SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)cited by 42
  17. 2024Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMscited by 40
  18. 2024Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLMcited by 35
  19. 2023FactCHD: Benchmarking Fact-Conflicting Hallucination Detectioncited by 29

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Prompt Design at Scale: How Format, Instruction Count, and Context Length Shape Instruction Adherence and Hallucination in Large Language Modelscited by 1