DataAgent: Evaluating Large Language Models' Ability to Answer Zero-Shot, Natural Language Queries

• URL: http://arxiv.org/abs/2404.00188v1
• Date: Fri, 29 Mar 2024 22:59:34 GMT
• Title: DataAgent: Evaluating Large Language Models' Ability to Answer Zero-Shot, Natural Language Queries
• Authors: Manit Mishra, Abderrahman Braham, Charles Marsom, Bryan Chung, Gavin Griffin, Dakshesh Sidnerlikar, Chatanya Sarin, Arjun Rajaram,
• Abstract summary: We evaluate OpenAI's GPT-3.5 as a "Language Data Scientist" (LDS) The model was tested on a diverse set of benchmark datasets to evaluate its performance across multiple standards.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional processes for analyzing datasets and extracting meaningful information are often time-consuming and laborious. Previous work has identified manual, repetitive coding and data collection as major obstacles that hinder data scientists from undertaking more nuanced labor and high-level projects. To combat this, we evaluated OpenAI's GPT-3.5 as a "Language Data Scientist" (LDS) that can extrapolate key findings, including correlations and basic information, from a given dataset. The model was tested on a diverse set of benchmark datasets to evaluate its performance across multiple standards, including data science code-generation based tasks involving libraries such as NumPy, Pandas, Scikit-Learn, and TensorFlow, and was broadly successful in correctly answering a given data science query related to the benchmark dataset. The LDS used various novel prompt engineering techniques to effectively answer a given question, including Chain-of-Thought reinforcement and SayCan prompt engineering. Our findings demonstrate great potential for leveraging Large Language Models for low-level, zero-shot data analysis.

Related papers

• Synthetic Data Generation with Large Language Models for Personalized Community Question Answering [47.300506002171275]
  We build Sy-SE-PQA based on an existing dataset, SE-PQA, which consists of questions and answers posted on the popular StackExchange communities. Our findings suggest that LLMs have high potential in generating data tailored to users' needs. The synthetic data can replace human-written training data, even if the generated data may contain incorrect information.
  arXiv  Detail & Related papers  (2024-10-29T16:19:08Z)
• SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models [54.78329741186446]
  We propose a novel paradigm that uses a code-based critic model to guide steps including question-code data construction, quality control, and complementary evaluation. Experiments across both in-domain and out-of-domain benchmarks in English and Chinese demonstrate the effectiveness of the proposed paradigm.
  arXiv  Detail & Related papers  (2024-08-28T06:33:03Z)
• DiscoveryBench: Towards Data-Driven Discovery with Large Language Models [50.36636396660163]
  We present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery. Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering. Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.
  arXiv  Detail & Related papers  (2024-07-01T18:58:22Z)
• Leveraging Large Language Models for Web Scraping [0.0]
  This research investigates a general-purpose accurate data scraping recipe for RAG models designed for language generation. To capture knowledge in a more modular and interpretable way, we use pre trained language models with a latent knowledge retriever.
  arXiv  Detail & Related papers  (2024-06-12T14:15:15Z)
• Elephants Never Forget: Testing Language Models for Memorization of Tabular Data [21.912611415307644]
  Large Language Models (LLMs) can be applied to a diverse set of tasks, but the critical issues of data contamination and memorization are often glossed over. We introduce a variety of different techniques to assess the degrees of contamination, including statistical tests for conditional distribution modeling and four tests that identify memorization.
  arXiv  Detail & Related papers  (2024-03-11T12:07:13Z)
• Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data [89.2410799619405]
  We introduce the Quantitative Reasoning with Data benchmark to evaluate Large Language Models' capability in statistical and causal reasoning with real-world data. The benchmark comprises a dataset of 411 questions accompanied by data sheets from textbooks, online learning materials, and academic papers. To compare models' quantitative reasoning abilities on data and text, we enrich the benchmark with an auxiliary set of 290 text-only questions, namely QRText.
  arXiv  Detail & Related papers  (2024-02-27T16:15:03Z)
• Capture the Flag: Uncovering Data Insights with Large Language Models [90.47038584812925]
  This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data. We propose a new evaluation methodology based on a "capture the flag" principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset.
  arXiv  Detail & Related papers  (2023-12-21T14:20:06Z)
• Utilising a Large Language Model to Annotate Subject Metadata: A Case Study in an Australian National Research Data Catalogue [18.325675189960833]
  In support of open and reproducible research, there has been a rapidly increasing number of datasets made available for research. As the availability of datasets increases, it becomes more important to have quality metadata for discovering and reusing them. This paper proposes to leverage large language models (LLMs) for cost-effective annotation of subject metadata through the LLM-based in-context learning.
  arXiv  Detail & Related papers  (2023-10-17T14:52:33Z)
• AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators [98.11286353828525]
  GPT-3.5 series models have demonstrated remarkable few-shot and zero-shot ability across various NLP tasks. We propose AnnoLLM, which adopts a two-step approach, explain-then-annotate. We build the first conversation-based information retrieval dataset employing AnnoLLM.
  arXiv  Detail & Related papers  (2023-03-29T17:03:21Z)
• Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density [58.64907136562178]
  We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods. We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
  arXiv  Detail & Related papers  (2021-11-02T15:48:28Z)

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.