298 citations · 754 across the 33 of their papers we have counts for
36 papers
Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions
Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio +4
Training large language models to follow instructions makes them perform better on a wide range of tasks and generally become more helpful. However, a perfectly helpful model will…
XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models
Paul Röttger, Hannah Rose Kirk, Bertie Vidgen +3
Without proper safeguards, large language models will readily follow malicious instructions and generate toxic content. This risk motivates safety efforts such as red-teaming and l…
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems
Patrick John Chia, Giuseppe Attanasio, Jacopo Tagliabue +5
Recommender Systems today are still mostly evaluated in terms of accuracy, with other aspects beyond the immediate relevance of recommendations, such as diversity, long-term user r…
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments
Federico Bianchi, Patrick John Chia, Ciro Greco +5
EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a m…
SocioProbe: What, When, and Where Language Models Learn about Sociodemographics
Anne Lauscher, Federico Bianchi, Samuel Bowman +1
Pre-trained language models (PLMs) have outperformed other NLP models on a wide range of tasks. Opting for a more thorough understanding of their capabilities and inner workings, r…
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
Federico Bianchi, Pratyusha Kalluri, Esin Durmus +7
Machine learning models that convert user-written text descriptions into images are now widely available online and used by millions of users to generate millions of images a day.…
"It's Not Just Hate'': A Multi-Dimensional Perspective on Detecting Harmful Speech Online
Federico Bianchi, Stefanie Anja Hills, Patricia Rossini +3
Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. W…
ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds
Tommaso Fornaciari, Dirk Hovy, Federico Bianchi
The most common ways to explore latent document dimensions are topic models and clustering methods. However, topic models have several drawbacks: e.g., they require us to choose th…
Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages
Paul Röttger, Debora Nozza, Federico Bianchi +1
Hate speech is a global phenomenon, but most hate speech datasets so far focus on English-language content. This hinders the development of more effective hate speech detection mod…
Is It Worth the (Environmental) Cost? Limited Evidence for Temporal Adaptation via Continuous Training
Giuseppe Attanasio, Debora Nozza, Federico Bianchi +1
Language is constantly changing and evolving, leaving language models to become quickly outdated. Consequently, we should continuously update our models with new data to expose the…
When and why vision-language models behave like bags-of-words, and what to do about it?
Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri +2
Despite the success of large vision and language models (VLMs) in many downstream applications, it is unclear how well they encode compositional information. Here, we create the At…
EvalRS: a Rounded Evaluation of Recommender Systems
Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel +4
Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of…
Real-Time Oil Leakage Detection on Aftermarket Motorcycle Damping System with Convolutional Neural Networks
Federico Bianchi, Stefano Speziali, Andrea Marini +5
In this work, we describe in detail how Deep Learning and Computer Vision can help to detect fault events of the AirTender system, an aftermarket motorcycle damping system componen…
"Does it come in black?" CLIP-like models are zero-shot recommenders
Patrick John Chia, Jacopo Tagliabue, Federico Bianchi +2
Product discovery is a crucial component for online shopping. However, item-to-item recommendations today do not allow users to explore changes along selected dimensions: given a q…
Contrastive language and vision learning of general fashion concepts
Patrick John Chia, Giuseppe Attanasio, Federico Bianchi +5
The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learnin…
Twitter-Demographer: A Flow-based Tool to Enrich Twitter Data
Federico Bianchi, Vincenzo Cutrona, Dirk Hovy
Twitter data have become essential to Natural Language Processing (NLP) and social science research, driving various scientific discoveries in recent years. However, the textual da…
Beyond NDCG: behavioral testing of recommender systems with RecList
Patrick John Chia, Jacopo Tagliabue, Federico Bianchi +2
As with most Machine Learning systems, recommender systems are typically evaluated through performance metrics computed over held-out data points. However, real-world behavior is u…
Language Invariant Properties in Natural Language Processing
Federico Bianchi, Debora Nozza, Dirk Hovy
Meaning is context-dependent, but many properties of language (should) remain the same even if we transform the context. For example, sentiment, entailment, or speaker properties s…
SWEAT: Scoring Polarization of Topics across Different Corpora
Federico Bianchi, Marco Marelli, Paolo Nicoli +1
Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the Sliced Word Embedding Association Test…
Solving Sensor Placement Problems In Real Water Distribution Networks Using Adiabatic Quantum Computation
Stefano Speziali, Federico Bianchi, Andrea Marini +6
Quantum annealing has emerged in the last few years as a promising quantum computing approach to solving large-scale combinatorial optimization problems. In this paper, we formulat…
Contrastive Language-Image Pre-training for the Italian Language
Federico Bianchi, Giuseppe Attanasio, Raphael Pisoni +3
CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts. The model is trained on a massive amount…
Multiclass classification of dephasing channels
Adriano M. Palmieri, Federico Bianchi, Matteo G. A. Paris +1
We address the use of neural networks (NNs) in classifying the environmental parameters of single-qubit dephasing channels. In particular, we investigate the performance of linear…
Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction
Federico Bianchi, Ciro Greco, Jacopo Tagliabue
We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engin…
Query2Prod2Vec Grounded Word Embeddings for eCommerce
Federico Bianchi, Jacopo Tagliabue, Bingqing Yu
We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent spac…