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Mehdi Ali

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3 papers

2 author rows

ECAI Conference 2025 Conference Paper

Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs

Mehdi Ali
Michael Fromm 0001
Klaudia Thellmann
Jan Ebert
Alexander Arno Weber
Richard Rutmann
Charvi Jain
Max Lübbering

We present two multilingual LLMs, Teuken 7B-base and Teuken 7B-instruct, designed to embrace Europe’s linguistic diversity by supporting all 24 official languages of the European Union. Trained on a dataset comprising around 60% non-English data and utilizing a custom multilingual tokenizer, our models address the limitations of existing Large Language Models (LLMs) that predominantly focus on English or a few high-resource languages. We detail the models’ development principles, i. e. , data composition, tokenizer optimization, and training methodologies. The models demonstrate strong performance across multilingual benchmarks, as evidenced by their performance on European versions of ARC, HellaSwag, and TruthfulQA.

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IJCAI Conference 2022 Conference Paper

Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract)

Mehdi Ali
Max Berrendorf
Mikhail Galkin
Veronika Thost
Tengfei Ma
Volker Tresp
Jens Lehmann

For many years, link prediction on knowledge. graphs has been a purely transductive task, not allowing for reasoning on unseen entities. Recently, increasing efforts are put into exploring semi- and fully inductive scenarios, enabling inference over unseen and emerging entities. Still, all these approaches only consider triple-based KGs, whereas their richer counterparts, hyper-relational KGs (e. g. , Wikidata), have not yet been properly studied. In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks. Our experiments on a novel set of benchmarks show that qualifiers over typed edges can lead to performance improvements of 6% of absolute gains (for the Hits@10 metric) compared to triple-only baselines. Our code is available at https: //github. com/mali-git/hyper_relational_ilp.

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JMLR Journal 2021 Journal Article

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

Mehdi Ali
Max Berrendorf
Charles Tapley Hoyt
Laurent Vermue
Sahand Sharifzadeh
Volker Tresp
Jens Lehmann

Recently, knowledge graph embeddings (KGEs) have received significant attention, and several software libraries have been developed for training and evaluation. While each of them addresses specific needs, we report on a community effort to a re-design and re-implementation of PyKEEN, one of the early KGE libraries. PyKEEN 1.0 enables users to compose knowledge graph embedding models based on a wide range of interaction models, training approaches, loss functions, and permits the explicit modeling of inverse relations. It allows users to measure each component's influence individually on the model's performance. Besides, an automatic memory optimization has been realized in order to optimally exploit the provided hardware. Through the integration of Optuna, extensive hyper-parameter optimization (HPO) functionalities are provided. [abs] [ pdf ][ bib ] &copy JMLR 2021. ( edit, beta )

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