2 citations · 10 across the 13 of their papers we have counts for
14 papers
Phonemes to the Rescue: Multilingual Tokenization Based on International Phonetic Alphabet
Milan Miletić, Julie Kallini, Ekaterina Shutova
Multilingual language models often exhibit performance disparities across languages that can arise as early as the tokenization stage. Widely-used subword tokenization approaches f…
Steerable Cultural Preference Optimization of Reward Models
Minsik Oh, Advit Deepak, Sophie Wu +2
It is essential for large language model (LLM) technology to serve many different cultural sub-communities in a manner that is acceptable to each community. However, research on LL…
Best-of-L: Cross-Lingual Reward Modeling for Mathematical Reasoning
Sara Rajaee, Rochelle Choenni, Ekaterina Shutova +1
While the reasoning abilities of large language models (LLMs) continue to advance, it remains unclear how such ability varies across languages in multilingual LLMs and whether diff…
Self-Alignment: Improving Alignment of Cultural Values in LLMs via In-Context Learning
Rochelle Choenni, Ekaterina Shutova
Improving the alignment of Large Language Models (LLMs) with respect to the cultural values that they encode has become an increasingly important topic. In this work, we study whet…
A framework for annotating and modelling intentions behind metaphor use
Gianluca Michelli, Xiaoyu Tong, Ekaterina Shutova
Metaphors are part of everyday language and shape the way in which we conceptualize the world. Moreover, they play a multifaceted role in communication, making their understanding…
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…
Are LLMs classical or nonmonotonic reasoners? Lessons from generics
Alina Leidinger, Robert van Rooij, Ekaterina Shutova
Recent scholarship on reasoning in LLMs has supplied evidence of impressive performance and flexible adaptation to machine generated or human feedback. Nonmonotonic reasoning, cruc…
The Echoes of Multilinguality: Tracing Cultural Value Shifts during LM Fine-tuning
Rochelle Choenni, Anne Lauscher, Ekaterina Shutova
Texts written in different languages reflect different culturally-dependent beliefs of their writers. Thus, we expect multilingual LMs (MLMs), that are jointly trained on a concate…
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.…
Gradient-based Parameter Selection for Efficient Fine-Tuning
Zhi Zhang, Qizhe Zhang, Zijun Gao +4
With the growing size of pre-trained models, full fine-tuning and storing all the parameters for various downstream tasks is costly and infeasible. In this paper, we propose a new…
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…
The language of prompting: What linguistic properties make a prompt successful?
Alina Leidinger, Robert van Rooij, Ekaterina Shutova
The latest generation of LLMs can be prompted to achieve impressive zero-shot or few-shot performance in many NLP tasks. However, since performance is highly sensitive to the choic…
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…
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…