9 citations · 14 across the 11 of their papers we have counts for
10 papers · 1 filter
Lemma Dilemma: On Lemma Generation Without Domain- or Language-Specific Training Data
Olia Toporkov, Alan Akbik, Rodrigo Agerri
Lemmatization is the task of transforming all words in a given text to their dictionary forms. While large language models (LLMs) have demonstrated their ability to achieve competi…
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…
Evaluating Design Decisions for Dual Encoder-based Entity Disambiguation
Susanna Rücker, Alan Akbik
Entity disambiguation (ED) is the task of linking mentions in text to corresponding entries in a knowledge base. Dual Encoders address this by embedding mentions and label candidat…
LM-PUB-QUIZ: A Comprehensive Framework for Zero-Shot Evaluation of Relational Knowledge in Language Models
Max Ploner, Jacek Wiland, Sebastian Pohl +1
Knowledge probing evaluates the extent to which a language model (LM) has acquired relational knowledge during its pre-training phase. It provides a cost-effective means of compari…
BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language Models
Jacek Wiland, Max Ploner, Alan Akbik
Knowledge probing assesses to which degree a language model (LM) has successfully learned relational knowledge during pre-training. Probing is an inexpensive way to compare LMs of…
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition
Jonas Golde, Felix Hamborg, Alan Akbik
Few-shot named entity recognition (NER) detects named entities within text using only a few annotated examples. One promising line of research is to leverage natural language descr…