Search Results for author: Federico Bianchi

Found 55 papers, 37 papers with code

MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?

no code implementations EACL (WASSA) 2021 Tommaso Fornaciari, Federico Bianchi, Debora Nozza, Dirk Hovy

The paper describes the MilaNLP team’s submission (Bocconi University, Milan) in the WASSA 2021 Shared Task on Empathy Detection and Emotion Classification.

Emotion Classification Multi-Task Learning

Pipelines for Social Bias Testing of Large Language Models

no code implementations BigScience (ACL) 2022 Debora Nozza, Federico Bianchi, Dirk Hovy

We hope to open a discussion on the best methodologies to handle social bias testing in language models.

FEEL-IT: Emotion and Sentiment Classification for the Italian Language

1 code implementation EACL (WASSA) 2021 Federico Bianchi, Debora Nozza, Dirk Hovy

While sentiment analysis is a popular task to understand people’s reactions online, we often need more nuanced information: is the post negative because the user is angry or sad?

Classification Sentiment Analysis +1

XLM-EMO: Multilingual Emotion Prediction in Social Media Text

1 code implementation WASSA (ACL) 2022 Federico Bianchi, Debora Nozza, Dirk Hovy

Detecting emotion in text allows social and computational scientists to study how people behave and react to online events.

TextGrad: Automatic "Differentiation" via Text

1 code implementation11 Jun 2024 Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, James Zou

Without modifying the framework, TextGrad improves the zero-shot accuracy of GPT-4o in Google-Proof Question Answering from $51\%$ to $55\%$, yields $20\%$ relative performance gain in optimizing LeetCode-Hard coding problem solutions, improves prompts for reasoning, designs new druglike small molecules with desirable in silico binding, and designs radiation oncology treatment plans with high specificity.

 Ranked #1 on on GPQA

Question Answering Specificity

Large Language Models are Vulnerable to Bait-and-Switch Attacks for Generating Harmful Content

no code implementations21 Feb 2024 Federico Bianchi, James Zou

The risks derived from large language models (LLMs) generating deceptive and damaging content have been the subject of considerable research, but even safe generations can lead to problematic downstream impacts.

How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis

1 code implementation8 Feb 2024 Federico Bianchi, Patrick John Chia, Mert Yuksekgonul, Jacopo Tagliabue, Dan Jurafsky, James Zou

We develop NegotiationArena: a flexible framework for evaluating and probing the negotiation abilities of LLM agents.

Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions

3 code implementations14 Sep 2023 Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, Paul Röttger, Dan Jurafsky, Tatsunori Hashimoto, James Zou

Training large language models to follow instructions makes them perform better on a wide range of tasks and generally become more helpful.

XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

1 code implementation2 Aug 2023 Paul Röttger, Hannah Rose Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, Dirk Hovy

In this paper, we introduce a new test suite called XSTest to identify such eXaggerated Safety behaviours in a systematic way.

Language Modelling

E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems

1 code implementation20 Apr 2023 Patrick John Chia, Giuseppe Attanasio, Jacopo Tagliabue, Federico Bianchi, Ciro Greco, Gabriel de Souza P. Moreira, Davide Eynard, Fahd Husain

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 retention and fairness, often taking a back seat.

Diversity Fairness +2

EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments

1 code implementation14 Apr 2023 Federico Bianchi, Patrick John Chia, Ciro Greco, Claudio Pomo, Gabriel Moreira, Davide Eynard, Fahd Husain, Jacopo Tagliabue

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 multitude of deployment scenarios.

Fairness Informativeness +1

SocioProbe: What, When, and Where Language Models Learn about Sociodemographics

1 code implementation8 Nov 2022 Anne Lauscher, Federico Bianchi, Samuel Bowman, Dirk Hovy

Our results show that PLMs do encode these sociodemographics, and that this knowledge is sometimes spread across the layers of some of the tested PLMs.

Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale

1 code implementation7 Nov 2022 Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, Aylin Caliskan

For example, we find cases of prompting for basic traits or social roles resulting in images reinforcing whiteness as ideal, prompting for occupations resulting in amplification of racial and gender disparities, and prompting for objects resulting in reification of American norms.

Text-to-Image Generation

ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds

1 code implementation26 Oct 2022 Tommaso Fornaciari, Dirk Hovy, Federico Bianchi

The most common ways to explore latent document dimensions are topic models and clustering methods.

Clustering Topic Models

Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages

1 code implementation20 Oct 2022 Paul Röttger, Debora Nozza, Federico Bianchi, Dirk Hovy

More data is needed, but annotating hateful content is expensive, time-consuming and potentially harmful to annotators.

Hate Speech Detection

Is It Worth the (Environmental) Cost? Limited Evidence for Temporal Adaptation via Continuous Training

no code implementations13 Oct 2022 Giuseppe Attanasio, Debora Nozza, Federico Bianchi, Dirk Hovy

Consequently, we should continuously update our models with new data to expose them to new events and facts.

When and why vision-language models behave like bags-of-words, and what to do about it?

1 code implementation4 Oct 2022 Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, James Zou

ARO consists of Visual Genome Attribution, to test the understanding of objects' properties; Visual Genome Relation, to test for relational understanding; and COCO & Flickr30k-Order, to test for order sensitivity.

Contrastive Learning Retrieval +1

Real-Time Oil Leakage Detection on Aftermarket Motorcycle Damping System with Convolutional Neural Networks

no code implementations10 Aug 2022 Federico Bianchi, Stefano Speziali, Andrea Marini, Massimiliano Proietti, Lorenzo Menculini, Alberto Garinei, Gabriele Bellani, Marcello Marconi

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 component.

EvalRS: a Rounded Evaluation of Recommender Systems

1 code implementation12 Jul 2022 Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel, Giuseppe Attanasio, Ciro Greco, Gabriel de Souza P. Moreira, Patrick John Chia

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 user interfaces.

Recommendation Systems

Twitter-Demographer: A Flow-based Tool to Enrich Twitter Data

1 code implementation26 Jan 2022 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.

Beyond NDCG: behavioral testing of recommender systems with RecList

3 code implementations18 Nov 2021 Patrick John Chia, Jacopo Tagliabue, Federico Bianchi, Chloe He, Brian Ko

As with most Machine Learning systems, recommender systems are typically evaluated through performance metrics computed over held-out data points.

Recommendation Systems

Language Invariant Properties in Natural Language Processing

1 code implementation nlppower (ACL) 2022 Federico Bianchi, Debora Nozza, Dirk Hovy

We introduce language invariant properties: i. e., properties that should not change when we transform text, and how they can be used to quantitatively evaluate the robustness of transformation algorithms.

Paraphrase Generation Translation

SWEAT: Scoring Polarization of Topics across Different Corpora

1 code implementation EMNLP 2021 Federico Bianchi, Marco Marelli, Paolo Nicoli, Matteo Palmonari

Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences.

Contrastive Language-Image Pre-training for the Italian Language

1 code implementation19 Aug 2021 Federico Bianchi, Giuseppe Attanasio, Raphael Pisoni, Silvia Terragni, Gabriele Sarti, Sri Lakshmi

CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts.

Image Retrieval Multi-label zero-shot learning +2

Query2Prod2Vec: Grounded Word Embeddings for eCommerce

1 code implementation NAACL 2021 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 space of products in a digital shop.

Word Embeddings

Words with Consistent Diachronic Usage Patterns are Learned Earlier: A Computational Analysis Using Temporally Aligned Word Embeddings

1 code implementation Cognitive Science 2021 Giovanni Cassani, Federico Bianchi, Marco Marelli

In this study, we use temporally aligned word embeddings and a large diachronic corpus of English to quantify language change in a data-driven, scalable way, which is grounded in language use.

Diachronic Word Embeddings Diversity +2

SIGIR 2021 E-Commerce Workshop Data Challenge

3 code implementations19 Apr 2021 Jacopo Tagliabue, Ciro Greco, Jean-Francis Roy, Bingqing Yu, Patrick John Chia, Federico Bianchi, Giovanni Cassani

The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations".

Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction

no code implementations NAACL 2021 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 engines; in particular, we explore the emergence of semantic generalization from unsupervised dense representations outside of synthetic environments.

Grounded language learning

Query2Prod2Vec Grounded Word Embeddings for eCommerce

1 code implementation2 Apr 2021 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 space of products in a digital shop.

Word Embeddings

Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario

no code implementations20 Jul 2020 Federico Bianchi, Jacopo Tagliabue, Bingqing Yu, Luca Bigon, Ciro Greco

This paper addresses the challenge of leveraging multiple embedding spaces for multi-shop personalization, proving that zero-shot inference is possible by transferring shopping intent from one website to another without manual intervention.

Knowledge Graph Embeddings and Explainable AI

no code implementations30 Apr 2020 Federico Bianchi, Gaetano Rossiello, Luca Costabello, Matteo Palmonari, Pasquale Minervini

Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces.

Knowledge Graph Embeddings

Cross-lingual Contextualized Topic Models with Zero-shot Learning

2 code implementations EACL 2021 Federico Bianchi, Silvia Terragni, Dirk Hovy, Debora Nozza, Elisabetta Fersini

They all cover the same content, but the linguistic differences make it impossible to use traditional, bag-of-word-based topic models.

Topic Models Transfer Learning +2

Compass-aligned Distributional Embeddings for Studying Semantic Differences across Corpora

1 code implementation13 Apr 2020 Federico Bianchi, Valerio Di Carlo, Paolo Nicoli, Matteo Palmonari

In this paper, we present a general framework to support cross-corpora language studies with word embeddings, where embeddings generated from different corpora can be compared to find correspondences and differences in meaning across the corpora.

Word Embeddings

Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

4 code implementations ACL 2021 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.

Sentence Embeddings Topic Models +1

"An Image is Worth a Thousand Features": Scalable Product Representations for In-Session Type-Ahead Personalization

no code implementations11 Mar 2020 Bingqing Yu, Jacopo Tagliabue, Ciro Greco, Federico Bianchi

We address the problem of personalizing query completion in a digital commerce setting, in which the bounce rate is typically high and recurring users are rare.

What the [MASK]? Making Sense of Language-Specific BERT Models

no code implementations5 Mar 2020 Debora Nozza, Federico Bianchi, Dirk Hovy

Driven by the potential of BERT models, the NLP community has started to investigate and generate an abundant number of BERT models that are trained on a particular language, and tested on a specific data domain and task.

Language Modelling

Training Temporal Word Embeddings with a Compass

1 code implementation5 Jun 2019 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.

Diachronic Word Embeddings Word Embeddings

Experimental neural network enhanced quantum tomography

no code implementations11 Apr 2019 Adriano Macarone Palmieri, Egor Kovlakov, Federico Bianchi, Dmitry Yudin, Stanislav Straupe, Jacob Biamonte, Sergei Kulik

We compared the neural network state reconstruction protocol with a protocol treating SPAM errors by process tomography, as well as to a SPAM-agnostic protocol with idealized measurements.

Reasoning over RDF Knowledge Bases using Deep Learning

2 code implementations9 Nov 2018 Monireh Ebrahimi, Md. Kamruzzaman Sarker, Federico Bianchi, Ning Xie, Derek Doran, Pascal Hitzler

Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field.

Knowledge Graphs

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