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Natalie Leesakul

2 papers hereh-index 5167 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2

Across the 2 of 2 papers where every author was matched, so the position is known.

fields
  • cs.CV1
  • cs.HC1

identity via Semantic Scholar / OpenAlex

most citedObjection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLM

13 citations · 13 across the 2 of their papers we have counts for

collaborators

2 papers

cs.HC2024★ 13 cited

Objection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLM

Eike Schneiders, Tina Seabrooke, Joshua Krook +4

Large Language Models (LLMs) are seemingly infiltrating every domain, and the legal context is no exception. In this paper, we present the results of three experiments (total N = 2…

cs.CV2021

Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning Architecture

Jimiama M. Mase, Natalie Leesakul, Fan Yang +2

Automatically understanding and recognising human affective states using images and computer vision can improve human-computer and human-robot interaction. However, privacy has bec…

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