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
MIRAGE uses model internals for faithful answer attribution in retrieval-augmented generation applications.
IT5s are the first encoder-decoder transformers pretrained on more than 40 billion Italian words.
We propose DecoderLens, a method to interpret the iterative refinement of representations in encoder-decoder Transformer models.
We introduce PECoRe, an interpretability framework for identifying context dependence in language model generations.
We introduce Retrieval and Attribute-Marking enhanced Prompting (RAMP) to perform attribute-controlled MT with multilingual LLMs.
We present Inseq, a Python library to democratize access to interpretability analyses of sequence generation models.
DivEMT is a publicly available post-editing study of Neural Machine Translation over a typologically diverse set of target languages.
We present the first CLIP model for the Italian Language (CLIP-Italian), trained on more than 1.4 million image-text pairs.