Search Results for author: Mehdi Ali

Found 6 papers, 4 papers with code

Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions?

no code implementations21 Feb 2024 Alexander Arno Weber, Klaudia Thellmann, Jan Ebert, Nicolas Flores-Herr, Jens Lehmann, Michael Fromm, Mehdi Ali

The adaption of multilingual pre-trained Large Language Models (LLMs) into eloquent and helpful assistants is essential to facilitate their use across different language regions.

Instruction Following

Tokenizer Choice For LLM Training: Negligible or Crucial?

no code implementations12 Oct 2023 Mehdi Ali, Michael Fromm, Klaudia Thellmann, Richard Rutmann, Max Lübbering, Johannes Leveling, Katrin Klug, Jan Ebert, Niclas Doll, Jasper Schulze Buschhoff, Charvi Jain, Alexander Arno Weber, Lena Jurkschat, Hammam Abdelwahab, Chelsea John, Pedro Ortiz Suarez, Malte Ostendorff, Samuel Weinbach, Rafet Sifa, Stefan Kesselheim, Nicolas Flores-Herr

The recent success of Large Language Models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as a blind spot.

Improving Inductive Link Prediction Using Hyper-Relational Facts

2 code implementations10 Jul 2021 Mehdi Ali, Max Berrendorf, Mikhail Galkin, Veronika Thost, Tengfei Ma, Volker Tresp, Jens Lehmann

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.

Inductive Link Prediction Knowledge Graphs

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

2 code implementations28 Jul 2020 Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, Jens Lehmann

Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.

 Ranked #1 on Link Prediction on WN18 (training time (s) metric)

Knowledge Graph Embedding Knowledge Graph Embeddings +1

The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability

1 code implementation28 Jan 2020 Mehdi Ali, Hajira Jabeen, Charles Tapley Hoyt, Jens Lehman

Therefore, we present the KEEN Universe, an ecosystem for knowledge graph embeddings that we have developed with a strong focus on reproducibility and transferability.

BIG-bench Machine Learning Fact Checking +4

Cannot find the paper you are looking for? You can Submit a new open access paper.