Abstract
Recent advances in large language models have revolutionized many sectors, including the database industry. One common challenge when dealing with large volumes of tabular data is the pervasive use of abbreviated column names, which can negatively impact performance on various data search, access, and understanding tasks. To address this issue, we introduce a new task, called NAMEGUESS, to expand column names (used in database schema) as a natural language generation problem. We create a training dataset of 384K abbreviated-expanded column pairs using a new data fabrication method and a human-annotated evaluation benchmark that includes 9.2K examples from real-world tables. To tackle the complexities associated with polysemy and ambiguity in NAMEGUESS, we enhance autoregressive language models by conditioning on table content and column header names - yielding a fine-tuned model (with 2.7B parameters) that matches human performance. Furthermore, we conduct a comprehensive analysis (on multiple LLMs) to validate the effectiveness of table content in NAMEGUESS and identify promising future opportunities. Code has been made available at https://github.com/amazon-science/nameguess.
Original language | English (US) |
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Title of host publication | EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings |
Editors | Houda Bouamor, Juan Pino, Kalika Bali |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 13276-13290 |
Number of pages | 15 |
ISBN (Electronic) | 9798891760608 |
State | Published - 2023 |
Externally published | Yes |
Event | 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 - Hybrid, Singapore, Singapore Duration: Dec 6 2023 → Dec 10 2023 |
Publication series
Name | EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings |
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Conference
Conference | 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 |
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Country/Territory | Singapore |
City | Hybrid, Singapore |
Period | 12/6/23 → 12/10/23 |
Bibliographical note
Publisher Copyright:©2023 Association for Computational Linguistics.