The results reveal that, although LLMs occasionally succeed in decoding communicative intentions, their performance is not attributable to human-like ToM reasoning, and offers insight into their interpretive biases, contributing to a deeper understanding of their linguistic capabilities.
A. Lombardi, Alessandro Lenci· Transactions of the Associat...· 0 citations
LLM evaluation is commonly performed either by prompting models to produce answers or by scoring candidate outputs with likelihood-based metrics. In multiple-choice QA, however, standard likelihood-based scoring is still conditioned on the question and answer set, and can therefore leverage the same task-conditioned an...
Alessandro Bondielli, Lucia C. Passaro, D. Bacciu et al.· 0 citations
Sparse AutoEncoders (SAEs) offer a promising way to inspect language model representations, but it is still unclear what kind of linguistic structure their latents expose. We use part-of-speech (PoS) categories as a controlled test case to study whether morpho-syntactic information is encoded by individual latents or b...
Alessandro Bondielli, Lucia C. Passaro, Serena Auriemma et al.· 0 citations
This work investigates cross-modal information flow in a video-based generative multiple-choice-like setting by applying a layer-wise causal intervention on video-text attention pathways and identifies a distinct pattern in temporal reasoning.
Davide Testa, Hugh Mee Wong, Alessandro Lenci et al.· 0 citations
This study explores the semantic competence of Italian BabyLMs, focusing on their sensitivity to semantic violations, using a minimal pair benchmark targeting semantic violations to evaluate the semantic abilities of BAMBI, a family of small-scale models trained on progressively larger and more complex datasets.
A. Suozzi, Luca Capone, Gianluca Lebani et al.· 0 citations
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