Oral presentation at CLiC-it 2024
We evaluate the rebus-solving capabilities of large language models on a new Italian dataset.
We propose Dynamic Activation Composition, an adaptive approach for multi-property activation steering of LLMs
We introduce Retrieval and Attribute-Marking enhanced Prompting (RAMP) to perform attribute-controlled MT with multilingual LLMs.