Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

Elisabeth Kirsten

dblp:372/1475 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
2since 2021 · last 2026
0009-0003-0680-8916ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Language models and text generation · 87% Trustworthy machine learning · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation
large language model		 1.012026
The Algorithmic Self-Portrait: Deconstructing Memory in ChatGPT · WWW 2026
Natural language and speech › Language models and text generation
memory		 1.012026
The Algorithmic Self-Portrait: Deconstructing Memory in ChatGPT · WWW 2026
Machine learning › Trustworthy machine learning
interpretability		 0.312026
The Algorithmic Self-Portrait: Deconstructing Memory in ChatGPT · WWW 2026
YearPublicationVenuePosition
2026 The Algorithmic Self-Portrait: Deconstructing Memory in ChatGPT
Abhisek Dash, Soumi Das, Elisabeth Kirsten, Qinyuan Wu, Sai Keerthana Karnam, Krishna P. Gummadi, Thorsten Holz, Muhammad Bilal Zafar, Savvas Zannettou
 WWW3
2025 The Impact of Inference Acceleration on Bias of LLMs
abstract
Elisabeth Kirsten, Ivan Habernal, Vedant Nanda, Muhammad Bilal Zafar. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.
Elisabeth Kirsten, Ivan Habernal, Vedant Nanda, Muhammad Bilal Zafar
 NAACL (Long Papers)1