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cs.CL2024

A Grounded Typology of Word Classes

Coleman Haley, Sharon Goldwater, Edoardo Ponti

We propose a grounded approach to meaning in language typology. We treat data from perceptual modalities, such as images, as a language-agnostic representation of meaning. Hence, w…

cs.CL2024

Mixtures of In-Context Learners

Giwon Hong, Emile van Krieken, Edoardo Ponti +2

In-context learning (ICL) adapts LLMs by providing demonstrations without fine-tuning the model parameters; however, it does not differentiate between demonstrations and quadratica…

cs.CL2024

Spectral Editing of Activations for Large Language Model Alignment

Yifu Qiu, Zheng Zhao, Yftah Ziser +3

Large language models (LLMs) often exhibit undesirable behaviours, such as generating untruthful or biased content. Editing their internal representations has been shown to be effe…

cs.CL2024

Cross-Lingual and Cross-Cultural Variation in Image Descriptions

Uri Berger, Edoardo M. Ponti

Do speakers of different languages talk differently about what they see? Behavioural and cognitive studies report cultural effects on perception; however, these are mostly limited…

cs.LG2024

Model Merging by Uncertainty-Based Gradient Matching

Nico Daheim, Thomas Möllenhoff, Edoardo Maria Ponti +2

Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail? Here, we connect the inaccuracy of weight…

cs.CL2024

Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference

Piotr Nawrot, Adrian Łańcucki, Marcin Chochowski +2

Transformers have emerged as the backbone of large language models (LLMs). However, generation remains inefficient due to the need to store in memory a cache of key-value represent…