activity
20232025
most citedDo large language models solve verbal analogies like children do?

2 citations · 3 across the 7 of their papers we have counts for

collaborators

5 papers

cs.CL2024

On the Evaluation Practices in Multilingual NLP: Can Machine Translation Offer an Alternative to Human Translations?

Rochelle Choenni, Sara Rajaee, Christof Monz +1

While multilingual language models (MLMs) have been trained on 100+ languages, they are typically only evaluated across a handful of them due to a lack of available test data in mo…

cs.CL20241 cited

Metaphor Understanding Challenge Dataset for LLMs

Xiaoyu Tong, Rochelle Choenni, Martha Lewis +1

Metaphors in natural language are a reflection of fundamental cognitive processes such as analogical reasoning and categorisation, and are deeply rooted in everyday communication.…

cs.CL2023

Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks

Rochelle Choenni, Ekaterina Shutova, Dan Garrette

Recent work has proposed explicitly inducing language-wise modularity in multilingual LMs via sparse fine-tuning (SFT) on per-language subnetworks as a means of better guiding cros…

cs.CL20232 cited

Do large language models solve verbal analogies like children do?

Claire E. Stevenson, Mathilde ter Veen, Rochelle Choenni +2

Analogy-making lies at the heart of human cognition. Adults solve analogies such as \textit{Horse belongs to stable like chicken belongs to ...?} by mapping relations (\textit{kept…

cs.CL2023

Probing LLMs for Joint Encoding of Linguistic Categories

Giulio Starace, Konstantinos Papakostas, Rochelle Choenni +4

Large Language Models (LLMs) exhibit impressive performance on a range of NLP tasks, due to the general-purpose linguistic knowledge acquired during pretraining. Existing model int…