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20242026
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cs.CL2026

Likelihood Ranking doesn't Scale Like Prompting in LLMs

Alessandro Bondielli, Lucia Passaro, Davide Bacciu +1

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, st…

cs.CL2026

Parts-of-Speech as Emergent Categories in SAE Latent Space

Alessandro Bondielli, Lucia Passaro, Serena Auriemma +1

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 p…

cs.CL2025

Charting a Decade of Computational Linguistics in Italy: The CLiC-it Corpus

Chiara Alzetta, Serena Auriemma, Alessandro Bondielli +5

Over the past decade, Computational Linguistics (CL) and Natural Language Processing (NLP) have evolved rapidly, especially with the advent of Transformer-based Large Language Mode…

cs.CL2025

All-in-one: Understanding and Generation in Multimodal Reasoning with the MAIA Benchmark

Davide Testa, Giovanni Bonetta, Raffaella Bernardi +5

We introduce MAIA (Multimodal AI Assessment), a native-Italian benchmark designed for fine-grained investigation of the reasoning abilities of visual language models on videos. MAI…

cs.CL2025

ExpliCa: Evaluating Explicit Causal Reasoning in Large Language Models

Martina Miliani, Serena Auriemma, Alessandro Bondielli +4

Large Language Models (LLMs) are increasingly used in tasks requiring interpretive and inferential accuracy. In this paper, we introduce ExpliCa, a new dataset for evaluating LLMs…

cs.CL2024

Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian

Serena Auriemma, Martina Miliani, Mauro Madeddu +3

Addressing the challenge of limited annotated data in specialized fields and low-resource languages is crucial for the effective use of Language Models (LMs). While most Large Lang…