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April 3, 20260 citationsOpen Access

Does Serialisation Format Affect LLM Performance on Tabular Classification? A Benchmarking Study

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SKSamiksha Khandelwal

Key Points

  • The aim is to investigate how different serialization formats influence the performance of LLMs in tabular classification tasks.
  • Tested three serialization formats: natural language sentences, JSON, and Markdown tables.
  • Used Flan-T5-XL model across three datasets: Adult Income, Heart Disease, and Bank Marketing.
  • Compared performance metrics to evaluate format effectiveness.
  • Significant performance differences of up to 14.4 percentage points on the Heart Disease dataset.
  • No single serialization format proved superior across all datasets.
  • Highlights the need for careful consideration of serialization formats in LLM design.

Abstract

While building an LLM classification pipeline, I noticed that converting tabularrows into text involves a design choice that often gets ignored — which format touse. This made me wonder whether that choice actually affects performance.In this study, I test three serialization formats — natural language sentences,JSON, and Markdown tables — using Flan-T5-XL across three datasets: AdultIncome, Heart Disease, and Bank Marketing. Results show that format choicedoes matter, with differences of up to 14.4 percentage points observed on theHeart Disease dataset. No single format consistently wins, suggesting that se-rialization format deserves more attention as a design parameter in LLM-basedtabular pipelines.

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Cite This Study

Samiksha Khandelwal (2026) studied this question.

synapsesocial.com/papers/69cf5e745a333a821460cd06https://doi.org/10.5281/zenodo.19342593
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