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Numerical Reasoning in Natural Language Processing

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  • UserNafise Moosavi (University of Sheffield) World_link
  • ClockFriday 10 November 2023, 12:00-13:00
  • HouseComputer Lab, SS03.

If you have a question about this talk, please contact Michael Schlichtkrull.

Numerical reasoning is a fundamental skill for language models to understand textual input in tasks such as text generation, question answering, and fact checking. The predominant approach for enhancing numerical reasoning has been scaling—larger models trained on more data tend to perform better on relevant benchmarks that require numerical reasoning. Unfortunately, this solution is often not accessible to the majority of end users of such models, as it is typically available through paid APIs or prominent research labs. In this presentation, we address the challenges of end-to-end numerical reasoning in various natural language processing (NLP) tasks. These challenges encompass evaluating the underlying reasoning skills while performing downstream applications and finding ways to improve these skills without resorting to scaling.

Dr. Nafise Sadat Moosavi is a Lecturer in Natural Language Processing at the Computer Science Department of the University of Sheffield. Before joining the University of Sheffield, she was a postdoctoral researcher at the Technical University of Darmstadt. She works on the limitation of language models to improve their fairness, reasoning, robustness, and efficiency. She co-founded and co-organizes SustaiNLP workshops and regularly serves as a senior area chair and area chair at *ACL conferences.

This talk is part of the NLIP Seminar Series series.

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