9 citations · 15 across the 6 of their papers we have counts for
6 papers
Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language Modeling
Ansar Aynetdinov, Patrick Haller, Alan Akbik
Recent research has shown that filtering massive English web corpora into high-quality subsets significantly improves training efficiency. However, for high-resource non-English la…
Pre-Training Curriculum for Multi-Token Prediction in Language Models
Ansar Aynetdinov, Alan Akbik
Multi-token prediction (MTP) is a recently proposed pre-training objective for language models. Rather than predicting only the next token (NTP), MTP predicts the next tokens a…
Don't Mesh with Me: Generating Constructive Solid Geometry Instead of Meshes by Fine-Tuning a Code-Generation LLM
Maximilian Mews, Ansar Aynetdinov, Vivian Schiller +2
While recent advancements in machine learning, such as LLMs, are revolutionizing software development and creative industries, they have had minimal impact on engineers designing m…
NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition
Elena Merdjanovska, Ansar Aynetdinov, Alan Akbik
Available training data for named entity recognition (NER) often contains a significant percentage of incorrect labels for entity types and entity boundaries. Such label noise pose…
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Ansar Aynetdinov, Alan Akbik
Instruction-tuned Large Language Models (LLMs) have recently showcased remarkable advancements in their ability to generate fitting responses to natural language instructions. Howe…
OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs
Patrick Haller, Ansar Aynetdinov, Alan Akbik
Instruction-tuned Large Language Models (LLMs) have recently showcased remarkable ability to generate fitting responses to natural language instructions. However, an open research…