545 citations · 606 across the 9 of their papers we have counts for
13 papers
Factual Confidence of LLMs: on Reliability and Robustness of Current Estimators
Matéo Mahaut, Laura Aina, Paula Czarnowska +3
Large Language Models (LLMs) tend to be unreliable in the factuality of their answers. To address this problem, NLP researchers have proposed a range of techniques to estimate LLM'…
Joint Lemmatization and Morphological Tagging with LEMMING
Thomas Muller, Ryan Cotterell, Alexander Fraser +1
We present LEMMING, a modular log-linear model that jointly models lemmatization and tagging and supports the integration of arbitrary global features. It is trainable on corpora a…
Labeled Morphological Segmentation with Semi-Markov Models
Ryan Cotterell, Thomas Müller, Alexander Fraser +1
We present labeled morphological segmentation, an alternative view of morphological processing that unifies several tasks. From an annotation standpoint, we additionally introduce…
Zero and Few-shot Learning for Author Profiling
Mara Chinea-Rios, Thomas Müller, Gretel Liz De la Peña Sarracén +2
Author profiling classifies author characteristics by analyzing how language is shared among people. In this work, we study that task from a low-resource viewpoint: using little or…
Active Few-Shot Learning with FASL
Thomas Müller, Guillermo Pérez-Torró, Angelo Basile +1
Recent advances in natural language processing (NLP) have led to strong text classification models for many tasks. However, still often thousands of examples are needed to train mo…
Few-Shot Learning with Siamese Networks and Label Tuning
Thomas Müller, Guillermo Pérez-Torró, Marc Franco-Salvador
We study the problem of building text classifiers with little or no training data, commonly known as zero and few-shot text classification. In recent years, an approach based on ne…