no code implementations • 29 Apr 2024 • Felix Drinkall, Eghbal Rahimikia, Janet B. Pierrehumbert, Stefan Zohren
Large language models (LLMs) are often trained on extensive, temporally indiscriminate text corpora, reflecting the lack of datasets with temporal metadata.
1 code implementation • 4 Apr 2024 • Fangru Lin, Daniel Altshuler, Janet B. Pierrehumbert
In this study, we probe different families of Large Language Models such as GPT-4 for their knowledge of the lexical semantics of scalar adjectives and one specific aspect of their pragmatics, namely scalar diversity.
1 code implementation • 23 Mar 2024 • Isabelle Lorge, Li Zhang, Xiaowen Dong, Janet B. Pierrehumbert
The rise of social media platforms has led to an increase in polarised online discussions, especially on political and socio-cultural topics such as elections and climate change.
no code implementations • 5 Feb 2024 • Fangru Lin, Emanuele La Malfa, Valentin Hofmann, Elle Michelle Yang, Anthony Cohn, Janet B. Pierrehumbert
Reasoning about asynchronous plans is challenging since it requires sequential and parallel planning to optimize time costs.
1 code implementation • 14 Dec 2022 • Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze
We propose a fully unsupervised method to detect bias in contextualized embeddings.
no code implementations • NAACL 2022 • Felix Drinkall, Stefan Zohren, Janet B. Pierrehumbert
We present a novel approach incorporating transformer-based language models into infectious disease modelling.
no code implementations • 16 Mar 2022 • Valentin Hofmann, Goran Glavaš, Nikola Ljubešić, Janet B. Pierrehumbert, Hinrich Schütze
While pretrained language models (PLMs) have been shown to possess a plethora of linguistic knowledge, the existing body of research has largely neglected extralinguistic knowledge, which is generally difficult to obtain by pretraining on text alone.
1 code implementation • NAACL 2022 • Paul Röttger, Bertie Vidgen, Dirk Hovy, Janet B. Pierrehumbert
To address this issue, we propose two contrasting paradigms for data annotation.
1 code implementation • Findings (NAACL) 2022 • Valentin Hofmann, Xiaowen Dong, Janet B. Pierrehumbert, Hinrich Schütze
The increasing polarization of online political discourse calls for computational tools that automatically detect and monitor ideological divides in social media.
2 code implementations • Findings (EMNLP) 2021 • Paul Röttger, Janet B. Pierrehumbert
Token-level analysis shows that temporal adaptation captures event-driven changes in language use in the downstream task, but not those changes that are actually relevant to task performance.
1 code implementation • ACL 2021 • Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze
How does the input segmentation of pretrained language models (PLMs) affect their interpretations of complex words?
3 code implementations • ACL 2021 • Paul Röttger, Bertram Vidgen, Dong Nguyen, Zeerak Waseem, Helen Margetts, Janet B. Pierrehumbert
Detecting online hate is a difficult task that even state-of-the-art models struggle with.
1 code implementation • ACL 2021 • Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze
Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts.
1 code implementation • EMNLP 2020 • Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze
Can pretrained language models (PLMs) generate derivationally complex words?
no code implementations • 8 Aug 2014 • Janet B. Pierrehumbert, Forrest Stonedahl, Robert Daland
But most linguistic changes are grassroots developments that originate with ordinary people.