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.
1 code implementation • 5 Feb 2024 • Fangru Lin, Emanuele La Malfa, Valentin Hofmann, Elle Michelle Yang, Anthony Cohn, Janet B. Pierrehumbert
Planning is a fundamental property of human intelligence.
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.