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Esin Durmus

Stanford University

40 papers hereh-index 3516.2k citations66 works total

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

author position
  • sole author1
  • first author7
  • middle author29

Across the 37 of 40 papers where every author was matched, so the position is known.

fields
  • cs.CL29
  • cs.LG4
  • cs.CY3
  • cs.AI2
  • cs.RO1
  • cs.SI1
affiliations
  • Stanford University
Homepage
same name
  • Esin Durmus — 3 papers, h 3

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
20192025
most citedEasily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale

298 citations · 1.1k across the 32 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CL2021

Towards Understanding Persuasion in Computational Argumentation

Esin Durmus

Opinion formation and persuasion in argumentation are affected by three major factors: the argument itself, the source of the argument, and the properties of the audience. Understa…

cs.CL2021

Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization

Faisal Ladhak, Esin Durmus, He He +2

Despite recent progress in abstractive summarization, systems still suffer from faithfulness errors. While prior work has proposed models that improve faithfulness, it is unclear w…

cs.LG2021

On the Opportunities and Risks of Foundation Models

Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…

cs.CL2021★ 52 cited

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…

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