Can Language Models Analyze Data? Evaluating Large Language Models for Question Answering over Datasets
📰 ArXiv cs.AI
Learn how to use large language models for question answering over datasets and evaluate their performance with different prompting strategies
Action Steps
- Load a dataset into a large language model using a library like Hugging Face's Transformers
- Evaluate the model's performance in directly answering questions given the dataset as input
- Generate SQL queries using the model to answer questions given a relational database schema
- Compare the performance of different prompting strategies on model accuracy
- Fine-tune the model for specific datasets or question types to improve performance
Who Needs to Know This
Data scientists and AI engineers can benefit from this knowledge to improve their data analysis workflows and build more efficient question answering systems
Key Insight
💡 Large language models can be effective in answering questions over datasets, but their performance depends on the prompting strategy and dataset characteristics
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🤖 Can large language models analyze data? New study evaluates their performance in question answering over datasets 📊
Full Article
Title: Can Language Models Analyze Data? Evaluating Large Language Models for Question Answering over Datasets
Abstract:
arXiv:2605.10419v1 Announce Type: cross 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
Abstract:
arXiv:2605.10419v1 Announce Type: cross 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
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