298 citations · 754 across the 33 of their papers we have counts for
18 papers · 1 filter
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…
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…
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…
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…
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…
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…
BERT Goes Shopping: Comparing Distributional Models for Product Representations
Federico Bianchi, Bingqing Yu, Jacopo Tagliabue
Word embeddings (e.g., word2vec) have been applied successfully to eCommerce products through~\textit{prod2vec}. Inspired by the recent performance improvements on several NLP task…
Cross-lingual Contextualized Topic Models with Zero-shot Learning
Federico Bianchi, Silvia Terragni, Dirk Hovy +2
Many data sets (e.g., reviews, forums, news, etc.) exist parallelly in multiple languages. They all cover the same content, but the linguistic differences make it impossible to use…
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence
Federico Bianchi, Silvia Terragni, Dirk Hovy
Topic models extract groups of words from documents, whose interpretation as a topic hopefully allows for a better understanding of the data. However, the resulting word groups are…
What the [MASK]? Making Sense of Language-Specific BERT Models
Debora Nozza, Federico Bianchi, Dirk Hovy
Recently, Natural Language Processing (NLP) has witnessed an impressive progress in many areas, due to the advent of novel, pretrained contextual representation models. In particul…
Training Temporal Word Embeddings with a Compass
Valerio Di Carlo, Federico Bianchi, Matteo Palmonari
Temporal word embeddings have been proposed to support the analysis of word meaning shifts during time and to study the evolution of languages. Different approaches have been propo…