Publications

Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents
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Reading Between the Prompts: How Stereotypes Shape LLM's Implicit Personalization
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Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models
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Pointwise Mutual Information as a Performance Gauge for Retrieval-Augmented Generation
Recent work suggests that large language models enhanced with retrieval-augmented generation are easily influenced by the order in …
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On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) with large language models (LLMs) has demonstrated strong performance in multilingual …
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Steering Large Language Models for Machine Translation Personalization
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QE4PE: Word-level Quality Estimation for Human Post-Editing
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MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs
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Non Verbis, Sed Rebus: Large Language Models Are Weak Solvers of Italian Rebuses
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EurekaRebus - Verbalized Rebus Solving with LLMs: A CALAMITA Challenge
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Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation
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The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models
Following multiple instructions is a crucial ability for large language models (LLMs). Evaluating this ability comes with significant …
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NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication
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Multi-property Steering of Large Language Models with Dynamic Activation Composition
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Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation
Ensuring the verifiability of model answers is a fundamental challenge for retrieval-augmented generation (RAG) in the question …
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BabyLM Challenge: Exploring the effect of variation sets on language model training efficiency
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Democratizing Advanced Attribution Analyses of Generative Language Models with the Inseq Toolkit
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Encoding of lexical tone in self-supervised models of spoken language
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DecoderLens: Layerwise Interpretation of Encoder-Decoder Transformers
In recent years, several interpretability methods have been proposed to interpret the inner workings of Transformer models at different …
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Quantifying the Plausibility of Context Reliance in Neural Machine Translation
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MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMs
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IT5: Text-to-text Pretraining for Italian Language Understanding and Generation
We introduce IT5, the first family of encoder-decoder transformer models pretrained specifically on Italian. We document and perform a …
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Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation
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Neural-agent Language Learning and Communication: Emergence of Dependency Length Minimization
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Endowing neural language learners with human-like biases: A case study on dependency length minimization
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Automatic Text Simplification: A Comparative Study in Italian for Children with Language Disorders
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A Functional Trade-off between Prosodic and Semantic Cues in Conveying Sarcasm
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Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models
Multilingual large-scale Pretrained Language Models (PLMs) have been shown to store considerable amounts of factual knowledge, but …
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Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off
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Wave to Syntax: Probing spoken language models for syntax
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RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation
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Inseq: An Interpretability Toolkit for Sequence Generation Models
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Contrastive Language-Image Pre-training for the Italian Language
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DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages
We introduce DivEMT, the first publicly available post-editing study of Neural Machine Translation (NMT) over a typologically diverse …
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Probing Linguistic Knowledge in Italian Neural Language Models across Language Varieties
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A Primer on the Inner Workings of Transformer-based Language Models
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