  This paper introduces the Contextual Language model for Accurate Imputation
Method (CLAIM), a novel strategy that capitalizes on the expansive knowledge
and reasoning capabilities of pre-trained large language models (LLMs) to
address missing data challenges in tabular datasets. Unlike traditional
imputation methods, which predominantly rely on numerical estimations, CLAIM
utilizes contextually relevant natural language descriptors to fill missing
values. This approach transforms datasets into natural language contextualized
formats that are inherently more aligned with LLMs' capabilities, thereby
facilitating the dual use of LLMs: first, to generate missing value
descriptors, and then, to fine-tune the LLM on the enriched dataset for
improved performance in downstream tasks. Our evaluations across diverse
datasets and missingness patterns reveal CLAIM's superior performance over
existing imputation techniques. Furthermore, our investigation into the
effectiveness of context-specific versus generic descriptors for missing data
highlights the importance of contextual accuracy in enhancing LLM performance
for data imputation. The results underscore CLAIM's potential to markedly
improve the reliability and quality of data analysis and machine learning
models, offering a more nuanced and effective solution for handling missing
data.
