3 papers
cs.CL2024
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'…
cs.CL2024
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…
cs.CL2024
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…