9 citations · 11 across the 6 of their papers we have counts for
6 papers
Error-Type-Aware Loss Reweighting for Robust Named Entity Recognition with Noisy LLM Labels
Elena Merdjanovska, Jonas Golde, Alan Akbik
Large language models are increasingly used to annotate datasets for training smaller, task-specialized models such as named entity recognition. While this method yields effective…
Self-Aware Knowledge Probing: Evaluating Language Models' Relational Knowledge through Confidence Calibration
Christopher Kissling, Elena Merdjanovska, Alan Akbik
Knowledge probing quantifies how much relational knowledge a language model (LM) has acquired during pre-training. Existing knowledge probes evaluate model capabilities through met…
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…