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researcher

Bhaskar Mitra

Microsoft

59 papers hereh-index 3610.2k citations111 works total

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

author position
  • sole author4
  • first author8
  • middle author38
  • last author7

Across the 57 of 59 papers where every author was matched, so the position is known.

fields
  • cs.IR53
  • cs.CL2
  • cs.HC2
  • cs.CY1
  • cs.LG1
affiliations
  • Microsoft
  • University College London
Homepage
same name
  • Bhaskar Mitra — 4 papers
  • Bhaskar Mitra — 4 papers, h 7
  • Bhaskar Mitra — 3 papers, h 5
  • Bhaskar Mitra — 2 papers, h 1
  • Bhaskar Mitra — 2 papers
  • Bhaskar Mitra — 2 papers

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
20162026
most citedOverview of the TREC 2020 deep learning track

117 citations · 311 across the 31 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CY2023

A Framework for Exploring the Consequences of AI-Mediated Enterprise Knowledge Access and Identifying Risks to Workers

Anna Gausen, Bhaskar Mitra, Siân Lindley

Organisations generate vast amounts of information, which has resulted in a long-term research effort into knowledge access systems for enterprise settings. Recent developments in…

cs.LG2023

DiSK: A Diffusion Model for Structured Knowledge

Ouail Kitouni, Niklas Nolte, James Hensman +1

Structured (dictionary-like) data presents challenges for left-to-right language models, as they can struggle with structured entities for a wide variety of reasons such as formatt…

cs.HC2023★ 3 cited

Co-audit: tools to help humans double-check AI-generated content

Andrew D. Gordon, Carina Negreanu, José Cambronero +9

Users are increasingly being warned to check AI-generated content for correctness. Still, as LLMs (and other generative models) generate more complex output, such as summaries, tab…

cs.IR2023

Large language models can accurately predict searcher preferences

Paul Thomas, Seth Spielman, Nick Craswell +1

Relevance labels, which indicate whether a search result is valuable to a searcher, are key to evaluating and optimising search systems. The best way to capture the true preference…

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