mlsanityData to 7 Oct 2026

Had enough of experts? Quantitative knowledge retrieval from large language models

arXiv 2402.07770 ↗First posted 12 Feb 2024cs.IR cs.CL stat.APcited by 4

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Abstract (arXiv)

Large language models (LLMs) have been extensively studied for their abilities to generate convincing natural language sequences, however their utility for quantitative information retrieval is less well understood. Here we explore the feasibility of LLMs as a mechanism for quantitative knowledge retrieval to aid two data analysis tasks: elicitation of prior distributions for Bayesian models and imputation of missing data. We introduce a framework that leverages LLMs to enhance Bayesian workflows by eliciting expert-like prior knowledge and imputing missing data. Tested on diverse datasets, this approach can improve predictive accuracy and reduce data requirements, offering significant potential in healthcare, environmental science and engineering applications. We discuss the implications and challenges of treating LLMs as 'experts'.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 35 of its 74 references that are in the map. First posted on arXiv 12 Feb 2024.

Builds on

35 of 74 references.

References · 35

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
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  3. 2019Language Models as Knowledge Bases?cited by 1,922
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  11. 2023In-Context Impersonation Reveals Large Language Models' Strengths and Biasescited by 154
  12. 2023Solving Math Word Problems by Combining Language Models With Symbolic Solverscited by 124
23 more
  1. 2023The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4cited by 112
  2. 2023Task Contamination: Language Models May Not Be Few-Shot Anymorecited by 98
  3. 2023Eliciting Human Preferences with Language Modelscited by 90
  4. 2023LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Modelscited by 80
  5. 2023Table-GPT: Table-tuned GPT for Diverse Table Taskscited by 69
  6. 2023ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Designcited by 66
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  14. 2021Prior knowledge elicitation: The past, present, and futurecited by 23
  15. 2023Jellyfish: A Large Language Model for Data Preprocessingcited by 20
  16. 2017The prior can generally only be understood in the context of the likelihoodcited by 18
  17. 2023How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Studycited by 16
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modellingcited by 15
  2. 2025Extracting Probabilistic Knowledge from Large Language Models for Bayesian Network Parameterizationcited by 15
  3. 2025OpenEstimate: Evaluating LLMs on Reasoning Under Uncertainty with Real-World Datacited by 3
  4. 2026Generative Priors Conditioned on Natural Language for Bayesian Inversion in PDEscited by 0