On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL

Yutong Shao, Ndapa Nakashole

Abstract
Structured data, prevalent in tables, databases, and knowledge graphs, poses a significant challenge in its representation. With the advent of large language models (LLMs), there has been a shift towards linearization-based methods, which process structured data as sequential token streams, diverging from approaches that explicitly model structure, often as a graph. Crucially, there remains a gap in our understanding of how these linearization-based methods handle structured data, which is inherently non-linear.This work investigates the linear handling of structured data in encoder-decoder language models, specifically T5. Our findings reveal the model’s ability to mimic human-designed processes such as schema linking and syntax prediction, indicating a deep, meaningful learning of structure beyond simple token sequencing. We also uncover insights into the model’s internal mechanisms, including the ego-centric nature of structure node encodings and the potential for model compression due to modality fusion redundancy. Overall, this work sheds light on the inner workings of linearization-based methods and could potentially provide guidance for future research.
Anthology ID:
2024.naacl-long.8
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
131–156
Language:
URL:
https://preview.aclanthology.org/add_missing_videos/2024.naacl-long.8/
DOI:
10.18653/v1/2024.naacl-long.8
Bibkey:
Cite (ACL):
Yutong Shao and Ndapa Nakashole. 2024. On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 131–156, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL (Shao & Nakashole, NAACL 2024)
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https://preview.aclanthology.org/add_missing_videos/2024.naacl-long.8.pdf
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