activity
20172026
most citedCombining Modular Skills in Multitask Learning

17 citations · 33 across the 12 of their papers we have counts for

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Showing 2024Show all

9 papers · 1 filter

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.LG2024

MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems

Yinsicheng Jiang, Yao Fu, Yeqi Huang +13

The sparse Mixture-of-Experts (MoE) architecture is increasingly favored for scaling Large Language Models (LLMs) efficiently, but it depends on heterogeneous compute and memory re…

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

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.AI2024

Post-hoc Reward Calibration: A Case Study on Length Bias

Zeyu Huang, Zihan Qiu, Zili Wang +2

Reinforcement Learning from Human Feedback aligns the outputs of Large Language Models with human values and preferences. Central to this process is the reward model (RM), which tr…

cs.CL20241 cited

Probing the Emergence of Cross-lingual Alignment during LLM Training

Hetong Wang, Pasquale Minervini, Edoardo M. Ponti

Multilingual Large Language Models (LLMs) achieve remarkable levels of zero-shot cross-lingual transfer performance. We speculate that this is predicated on their ability to align…