Alan Akbik

HU Berlin, Machine Learning

Alan focuses on research in machine learning (ML) and natural language processing (NLP), with the goal of giving machines the ability to understand and use human language. This spans research topics such as neural language modeling, sample-efficient learning and semantic parsing, as well as application areas in large-scale text analytics. Together with his group and the open source community, he develops the NLP framework Flair ( that allows anyone to use state-of-the-art NLP methods in their research or applications.

At SCIoI, Alan works at Project A002, Project 44, and Project 45.


SCIoI Publications:

Ziletti, A., Akbik, A., Berns, C., Herold, T., Legler, M., & Viell, M. (2022). Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learning. NAACL, 176–187.
Wiland, J., Ploner, M., & Akbik, A. (2024). BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language Models. NAACL 2024.
Rücker, S., & Akbik, A. (2024). CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset. EMNLP 2023.
Ploner, M., & Akbik, A. (2024). Parameter-Efficient Fine-Tuning: Is There An Optimal Subset of Parameters to Tune? EACL 2024.
Milich, M., & Akbik, A. (2023). ZELDA: A Comprehensive Benchmark for Supervised Entity Disambiguation. EACL 2023.
Haller, P., Golde, J., & Akbik, A. (2024). PECC: Problem Extraction and Coding Challenges. COLING 2024.
Haller, P., Aynetdinov, A., & Akbik, A. (2024). OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs. NAACL 2024.
Golde, J., Hamborg, F., & Akbik, A. (2024). Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition. EACL 2024.
Golde, J., Haller, P., Hamborg, F., Risch, J., & Akbik, A. (2024). Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs. EMNLP 2023.
Garbaciauskas, L., Ploner, M., & Akbik, A. (2024). Choose Your Transformer: Improved Transferability Estimation of Transformer Models on Classification Tasks. ACL 2024.
Dallabetta, M., Dobberstein, C., Breiding, A., & Akbik, A. (2024). Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions. ACL 2024.