3 citations · 10 across the 7 of their papers we have counts for
11 papers
Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change
Murathan Kurfalı, Shorouq Zahra, Joakim Nivre +1
Climate-Eval is a comprehensive benchmark designed to evaluate natural language processing models across a broad range of tasks related to climate change. Climate-Eval aggregates e…
The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation
Fredrik Carlsson, Fangyu Liu, Daniel Ward +2
This paper introduces the counter-intuitive generalization results of overfitting pre-trained large language models (LLMs) on very small datasets. In the setting of open-ended text…
Lightweight Connective Detection Using Gradient Boosting
Mustafa Erolcan Er, Murathan Kurfalı, Deniz Zeyrek
In this work, we introduce a lightweight discourse connective detection system. Employing gradient boosting trained on straightforward, low-complexity features, this proposed appro…
Evaluation of really good grammatical error correction
Robert Östling, Katarina Gillholm, Murathan Kurfalı +2
Although rarely stated, in practice, Grammatical Error Correction (GEC) encompasses various models with distinct objectives, ranging from grammatical error detection to improving f…
Language Embeddings Sometimes Contain Typological Generalizations
Robert Östling, Murathan Kurfalı
To what extent can neural network models learn generalizations about language structure, and how do we find out what they have learned? We explore these questions by training neura…
Probing Multilingual Language Models for Discourse
Murathan Kurfalı, Robert Östling
Pre-trained multilingual language models have become an important building block in multilingual natural language processing. In the present paper, we investigate a range of such m…