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Robert D. Hawkins

28 papers hereh-index 261.9k citations72 works total

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

author position
  • first author7
  • middle author17
  • last author4

Across the 28 of 28 papers where every author was matched, so the position is known.

fields
  • cs.CL21
  • cs.AI4
  • cs.IT1
  • cs.MA1
  • cs.OH1
same name
  • Robert D. Hawkins — 5 papers, h 2
  • Robert D. Hawkins — 3 papers, h 3
  • Robert D. Hawkins — 3 papers, h 1
  • Robert D. Hawkins — 2 papers
  • Robert D. Hawkins — 2 papers, h 2
  • Robert D. Hawkins — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20132023
most citedBootstrap Methods for the Empirical Study of Decision-Making and Information Flows in Social Systems

61 citations · 160 across the 21 of their papers we have counts for

collaborators
Showing 2022 · cs.CLShow all

4 papers · 2 filters

cs.CL2022

Abstract Visual Reasoning with Tangram Shapes

Anya Ji, Noriyuki Kojima, Noah Rush +4

We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines. Drawing on the history of tangram puzzles as stimuli in cognitive science, we build…

cs.CL2022★ 6 cited

Identifying concept libraries from language about object structure

Catherine Wong, William P. McCarthy, Gabriel Grand +5

Our understanding of the visual world goes beyond naming objects, encompassing our ability to parse objects into meaningful parts, attributes, and relations. In this work, we lever…

cs.CL2022★ 1 cited

Mixed-effects transformers for hierarchical adaptation

Julia White, Noah Goodman, Robert Hawkins

Language use differs dramatically from context to context. To some degree, modern language models like GPT-3 are able to account for such variance by conditioning on a string of pr…

cs.CL2022

Probing BERT's priors with serial reproduction chains

Takateru Yamakoshi, Thomas L. Griffiths, Robert D. Hawkins

Sampling is a promising bottom-up method for exposing what generative models have learned about language, but it remains unclear how to generate representative samples from popular…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.