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arXiv Computation and Language By Junwei Ma, Arun Kumar Chithanar, Palash Goyal, Chul Lee

Table Serialization Determines Which Relations Language Models Preserve

Read the original on arXiv Computation and Language →

The paper investigates how the linear serialization of tables influences the ability of instruction‑tuned language models to perform table lookup tasks. By comparing row‑major and column‑major formats on synthetic 256‑cell tables of varying aspect ratios, the authors find that the optimal serialization depends on table shape: row‑major works best for tall tables, while column‑major excels on wide tables. Error analysis reveals a complementary pattern—row‑major preserves the correct row but often misses the target column, whereas column‑major preserves the correct column but sometimes loses the row—leading the authors to introduce the concept of relational locality. "whyItMatters":"The study shows that failures attributed to table width are actually driven by the chosen serialization format, highlighting the importance of representation choices for language model performance on tabular data."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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