1 citations · 1 across the 6 of their papers we have counts for
6 papers · 1 filter
How Much Noise Can BERT Handle? Insights from Multilingual Sentence Difficulty Detection
Nouran Khallaf, Serge Sharoff
Noisy training data can significantly degrade the performance of language-model-based classifiers, particularly in non-topical classification tasks. In this study we designed a met…
To Predict or Not to Predict? Towards reliable uncertainty estimation in the presence of noise
Nouran Khallaf, Serge Sharoff
This study examines the role of uncertainty estimation (UE) methods in multilingual text classification under noisy and non-topical conditions. Using a complex-vs-simple sentence c…
Almost Clinical: Linguistic properties of synthetic electronic health records
Serge Sharoff, John Baker, David Francis Hunt +1
This study evaluates the linguistic and clinical suitability of synthetic electronic health records in mental health. First, we describe the rationale and the methodology for creat…
Can LLM Reasoning Be Trusted? A Comparative Study: Using Human Benchmarking on Statistical Tasks
Crish Nagarkar, Leonid Bogachev, Serge Sharoff
This paper investigates the ability of large language models (LLMs) to solve statistical tasks, as well as their capacity to assess the quality of reasoning. While state-of-the-art…
Controlling Out-of-Domain Gaps in LLMs for Genre Classification and Generated Text Detection
Dmitri Roussinov, Serge Sharoff, Nadezhda Puchnina
This study demonstrates that the modern generation of Large Language Models (LLMs, such as GPT-4) suffers from the same out-of-domain (OOD) performance gap observed in prior resear…
BERT Goes Off-Topic: Investigating the Domain Transfer Challenge using Genre Classification
Dmitri Roussinov, Serge Sharoff
While performance of many text classification tasks has been recently improved due to Pre-trained Language Models (PLMs), in this paper we show that they still suffer from a perfor…