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TOWARDS CONTROLLED TABLE-TO-TEXT GENERATION WITH SCIENTIFIC REASONING

DOI:
10.60864/qe75-eq79
Citation Author(s):
Submitted by:
Zhixin Guo
Last updated:
6 June 2024 - 10:33am
Document Type:
Presentation Slides
Presenters:
Zhixin Guo
Paper Code:
SLP-L3.1
 

The sheer volume of scientific experimental results and complex technical statements, often presented in tabular formats, presents a formidable barrier to individuals acquiring preferred information. The realms of scientific reasoning and content generation that adhere to user preferences encounter distinct challenges. In this work, we present a new task for generating fluent and logical descriptions that match user preferences over scientific tabular data, aiming to automate scientific document analysis. To facilitate research in this direction, we construct a new challenging dataset CTRLSciTab consisting of table-description pairs extracted from the scientific literature, with highlighted cells and corresponding domain-specific knowledge base. We evaluated popular pre-trained language models to establish a baseline and proposed a novel architecture outperforming competing approaches. The results showed that large models struggle to produce accurate content that aligns with user preferences. As the first of its kind, our work should motivate further research in scientific domains.

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