Can Language Models Analyze Data? Evaluating Large Language Models for Question Answering over Datasets

• URL: http://arxiv.org/abs/2605.10419v1
• Date: Mon, 11 May 2026 11:54:47 GMT
• Title: Can Language Models Analyze Data? Evaluating Large Language Models for Question Answering over Datasets
• Abstract summary: This paper investigates the effectiveness of large language models (LLMs) in answering questions over datasets.<n> Experiments are conducted on two datasets containing questions of varying difficulty.<n>Results demonstrate the strong performance of large LLMs, while highlighting the limitations of smaller, more cost-efficient models.
• Score: 0.03683202928838612
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper investigates the effectiveness of large language models (LLMs) in answering questions over datasets. We examine their performance in two scenarios: (a) directly answering questions given a dataset file as input, and (b) generating SQL queries to answer questions given the schema of a relational database. We also evaluate the impact of different prompting strategies on model performance. The study includes both state-of-the-art LLMs and smaller language models that require fewer resources and operate at lower computational and financial cost. Experiments are conducted on two datasets containing questions of varying difficulty. The results demonstrate the strong performance of large LLMs, while highlighting the limitations of smaller, more cost-efficient models. These findings contribute to a better understanding of how LLMs can be utilized in data analytics tasks and their associated limitations.

Related papers

• Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities [16.09389994769264]
  We introduce DataGovBench, a benchmark designed to evaluate Large Language Models in practical scenarios.<n> Comprehensive experiments with state-of-the-art LLMs reveal significant performance gaps across both tasks.<n>DataGovBench provides a challenging benchmark for advancing research on LLMs capable of both answering analytical queries and discovering insights from data.
  arXiv  Detail & Related papers  (2026-07-07T16:43:05Z)
• Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study [55.09905978813599]
  Large Language Models (LLMs) hold promise in automating data analysis tasks.<n>Yet open-source models face significant limitations in these kinds of reasoning-intensive scenarios.<n>In this work, we investigate strategies to enhance the data analysis capabilities of open-source LLMs.
  arXiv  Detail & Related papers  (2025-06-24T17:04:23Z)
• IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis [60.32962597618861]
  IDA-Bench is a novel benchmark evaluating large language models in multi-round interactive scenarios.<n>Agent performance is judged by comparing its final numerical output to the human-derived baseline.<n>Even state-of-the-art coding agents (like Claude-3.7-thinking) succeed on 50% of the tasks, highlighting limitations not evident in single-turn tests.
  arXiv  Detail & Related papers  (2025-05-23T09:37:52Z)
• LLMs as Data Annotators: How Close Are We to Human Performance [47.61698665650761]
  Manual annotation of data is labor-intensive, time-consuming, and costly.<n>In-context learning (ICL) in which some examples related to the task are given in the prompt can lead to inefficiencies and suboptimal model performance.<n>This paper presents experiments comparing several LLMs, considering different embedding models, across various datasets for the Named Entity Recognition (NER) task.
  arXiv  Detail & Related papers  (2025-04-21T11:11:07Z)
• New Dataset and Methods for Fine-Grained Compositional Referring Expression Comprehension via Specialist-MLLM Collaboration [49.180693704510006]
  Referring Expression (REC) is a cross-modal task that evaluates the interplay of language understanding, image comprehension, and language-to-image grounding.<n>It serves as an essential testing ground for Multimodal Large Language Models (MLLMs)
  arXiv  Detail & Related papers  (2025-02-27T13:58:44Z)
• Enhancing Code Generation for Low-Resource Languages: No Silver Bullet [55.39571645315926]
  Large Language Models (LLMs) rely on large and diverse datasets to learn syntax, semantics, and usage patterns of programming languages.<n>For low-resource languages, the limited availability of such data hampers the models' ability to generalize effectively.<n>We present an empirical study investigating the effectiveness of several approaches for boosting LLMs' performance on low-resource languages.
  arXiv  Detail & Related papers  (2025-01-31T12:23:28Z)
• 60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering [50.12622877002846]
  Large language models (LLMs) encode extensive world knowledge through pre-training on massive datasets, which can be fine-tuned for the question-answering (QA) task.<n>We categorize supervised fine-tuning (SFT) data based on the extent of knowledge memorized by the pretrained LLMs.<n>Our experiments show that as few as 60 data points during the SFT stage can activate the knowledge encoded during pre-training, enabling LLMs to perform the QA task.
  arXiv  Detail & Related papers  (2024-09-24T07:38:38Z)
• Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data [39.29778853025738]
  Large Language Models (LLMs) have been achieving competent performance on a wide range of downstream tasks. This paper proposes a framework, Learning to Reduce, that fine-tunes a language model with On-Policy Learning to generate a reduced version of an input structured data.
  arXiv  Detail & Related papers  (2024-07-03T01:51:50Z)
• Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems [76.69936664916061]
  We study how the number of LM calls affects the performance of Vote and Filter-Vote. We find, surprisingly, that across multiple language tasks, the performance of both Vote and Filter-Vote can first increase but then decrease as a function of the number of LM calls.
  arXiv  Detail & Related papers  (2024-03-04T19:12:48Z)
• Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Models [42.16047343029512]
  Large Language Models (LLMs) have been widely used as general-purpose AI agents. We propose a framework, Learning to Reduce, that fine-tunes a language model to generate a reduced version of an input context. We show that our model achieves comparable accuracies in selecting the relevant evidence from an input context.
  arXiv  Detail & Related papers  (2024-02-22T00:41:23Z)
• On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering [25.57202500348071]
  This study introduces a new long-form database question answering dataset designed to evaluate how Large Language Models interact with a database. The task requires LLMs to strategically generate multiplesql queries to retrieve sufficient data from a database, to reason with the acquired context, and to synthesize them into a comprehensive analytical narrative. We propose and evaluate two interaction strategies, and provide a fine-grained analysis of the individual stages within the interaction.
  arXiv  Detail & Related papers  (2023-11-16T09:55:07Z)
• Improving Small Language Models on PubMedQA via Generative Data Augmentation [4.96649519549027]
  Large Language Models (LLMs) have made remarkable advancements in the field of natural language processing. Small Language Models (SLMs) are known for their efficiency, but they often struggle with limited capacity and training data. We introduce a novel method aimed at improving SLMs in the medical domain using LLM-based generative data augmentation.
  arXiv  Detail & Related papers  (2023-05-12T23:49:23Z)

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.