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Demo Data from EDBT 2026 / reviewers in the wild / expert

Yosuke Kishinami

dblp:329/6525 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2026
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-author · 2 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 · 50% Information extraction and text analysis · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation › large language model › knowledge in language models › memorization
memorization in language models		 1.012026
Revisiting Non-Verbatim Memorization in Large Language Models: The Role of Entity Surface Forms · ACL (1) 2026
Natural language and speech › Information extraction and text analysis
named entity processing		 1.012026
Revisiting Non-Verbatim Memorization in Large Language Models: The Role of Entity Surface Forms · ACL (1) 2026

Methods — techniques the papers use, named apart from their topics

probing · 1.0
YearPublicationVenuePosition
2026 Revisiting Non-Verbatim Memorization in Large Language Models: The Role of Entity Surface Forms
abstract
Yuto Nishida, Naoki Shikoda, Yosuke Kishinami, Ryo Fujii, Makoto Morishita, Hidetaka Kamigaito, Taro Watanabe. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Yuto Nishida, Naoki Shikoda, Yosuke Kishinami, Ryo Fujii, Makoto Morishita, Hidetaka Kamigaito, Taro Watanabe
 ACL (1)3
2022 Target-Guided Open-Domain Conversation Planning
abstract
Prior studies addressing target-oriented conversational tasks lack a crucial notion that has been intensively studied in the context of goal-oriented artificial intelligence agents, namely, planning. In this study, we propose the task of Target-Guided Open-Domain Conversation Planning (TGCP) task to evaluate whether neural conversational agents have goal-oriented conversation planning abilities. Using the TGCP task, we investigate the conversation planning abilities of existing retrieval models and recent strong generative models. The experimental results reveal the challenges facing current technology.
Yosuke Kishinami, Reina Akama, Shiki Sato, Ryoko Tokuhisa, Jun Suzuki 0001, Kentaro Inui
 COLING1