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cs.CL2025
Context Parametrization with Compositional Adapters
Josip Jukić, Martin Tutek, Jan Šnajder
Large language models (LLMs) often seamlessly adapt to new tasks through in-context learning (ICL) or supervised fine-tuning (SFT). However, ICL is inefficient when handling many d…
cs.CL2025
Improving Data and Parameter Efficiency of Neural Language Models Using Representation Analysis
Josip Jukić
This thesis addresses challenges related to data and parameter efficiency in neural language models, with a focus on representation analysis and the introduction of new optimizatio…
cs.CL2024
Disentangling Latent Shifts of In-Context Learning with Weak Supervision
Josip Jukić, Jan Šnajder
In-context learning (ICL) enables large language models to perform few-shot learning by conditioning on labeled examples in the prompt. Despite its flexibility, ICL suffers from in…