This content will become publicly available on March 31, 2026
ReBERT: LLM for Gate-Level to Word-Level Reverse Engineering
- Award ID(s):
- 2322713
- PAR ID:
- 10633496
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 978-3-9826741-0-0
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Location:
- Lyon, France
- Sponsoring Org:
- National Science Foundation
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Certainty and uncertainty are fundamental to science communication. Hedges have widely been used as proxies for uncertainty. However, certainty is a complex construct, with authors expressing not only the degree but the type and aspects of uncertainty in order to give the reader a certain impression of what is known. Here, we introduce a new study of certainty that models both the level and the aspects of certainty in scientific findings. Using a new dataset of 2167 annotated scientific findings, we demonstrate that hedges alone account for only a partial explanation of certainty. We show that both the overall certainty and individual aspects can be predicted with pre-trained language models, providing a more complete picture of the author’s intended communication. Downstream analyses on 431K scientific findings from news and scientific abstracts demonstrate that modeling sentence-level and aspect-level certainty is meaningful for areas like science communication. Both the model and datasets used in this paper are released at https://blablablab.si.umich.edu/projects/certainty/.more » « less
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