Loris Schoenegger

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Dive into the research topics where Loris Schoenegger is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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1 Finished

Cognitive Plausibility of Deep Learning Language Models
Thoma, L. (Project Lead), Blevins, T. (Co-Lead), Schoenegger, L. (Project Staff), Weyers, I. (Project Lead) & Çano, E. (Affiliated Project Staff)
1/08/22 → 31/01/25
Project: Research funding

3 Paper
2 Contribution to proceedings

Compact Example-Based Explanations for Language Models
Schoenegger, L. & Roth, B., Jul 2026, Findings of the Association for Computational Linguistics: ACL 2026. Association for Computational Linguistics (ACL)
Publications: Contribution to book › Contribution to proceedings › Peer Reviewed

Open Access
Influential Training Data Retrieval for Explaining Verbalized Confidence of LLMs
Xia, Y., Schoenegger, L. & Roth, B., 29 Mar 2026, The 48th European Conference on Information Retrieval (ECIR 2026) .
Publications: Contribution to book › Contribution to proceedings › Peer Reviewed
Select or Project? Evaluating Lower-dimensional Vectors for LLM Training Data Explanations
Hinterleitner, L., Schoenegger, L. & Roth, B., 2026, (Accepted/In press).
Publications: Contribution to conference › Paper › Peer Reviewed

Open Access

Loris Schoenegger

Fingerprint

Collaborations and top research areas from the last five years

Dive into details

Cognitive Plausibility of Deep Learning Language Models

Compact Example-Based Explanations for Language Models

Influential Training Data Retrieval for Explaining Verbalized Confidence of LLMs

Select or Project? Evaluating Lower-dimensional Vectors for LLM Training Data Explanations

Loris Schoenegger

Fingerprint

Collaborations and top research areas from the last five years

Projects

Cognitive Plausibility of Deep Learning Language Models

Publications

Compact Example-Based Explanations for Language Models

Influential Training Data Retrieval for Explaining Verbalized Confidence of LLMs

Select or Project? Evaluating Lower-dimensional Vectors for LLM Training Data Explanations