This repository provides a comprehensive experimental framework for evaluating Large Language Models (LLMs) in the context of missing data imputation. The study investigates both performance and behavioral aspects, including hallucination effects and control mechanisms.

We evaluate five LLM families across different architectures, including:

• Mistral
• Claude
• GPT
• Gemini

These models are benchmarked against traditional and state-of-the-art imputation methods:

• k-Nearest Neighbors (kNN)
• Multivariate Imputation by Chained Equations (MICE)
• missForest
• Stacked Autoencoder Imputation (SAEI)
• TabPFN

Experiments are conducted under the three standard missing data mechanisms:

• Missing Completely at Random (MCAR)
• Missing at Random (MAR)
• Missing Not at Random (MNAR)

Empirical results indicate that Claude 4.5 Sonnet and Gemini 3.0 Flash consistently outperform baseline methods across all missingness mechanisms.

In terms of computational efficiency, LLM-based approaches require significantly more resources compared to traditional imputation methods.

Install the required dependencies:

pip install -r requirements.txt

We recommend using a dedicated virtual environment that could be found here.

source LLM/bin/activate # On Linux/macOS
.\LLM\Scripts\activate   # On Windows

LLMs introduce a substantial computational overhead compared to classical methods. This includes:

• Higher latency due to API calls

• Increased monetary cost (depending on provider)

• Dependency on external services

These aspects should be considered when deploying LLM-based imputation in practice.

This work has been submitted to IEEE Transactions on Knowledge and Data. Further details will be provided upon publication.