INTEGRATOR
INTEGRATOR logo
Dirk Hovy, scientific director of DMI and Professor of computer science, has won an ERC starting grant of 1.5mln euros. His project INTEGRATOR, funded under grant agreement 949944, introduces demographic factors into language processing systems, which will improve algorithmic performance, avoid racism, sexism, and ageism, and open up new applications. What if I wrote that “winning an ERC Grant, Dirk Hovy got a sick result?”. Those familiar with the use of “sick” as a synonym for “great” or “awesome” among teenagers would think that Bocconi Knowledge hired a very young writer (or someone posing as such). The rest would think I went crazy. Current artificial intelligence-based language systems wouldn’t have a clue. “Natural language processing (NLP) technologies,” Prof. Hovy says, “fail to account for demographics both in understanding language and in generating it. And this failure prevents us from reaching human-like performance. It limits possible future applications and it introduces systematic bias against underrepresented demographic groups”.
🗞️🗞️ Related articles featured in Corriere Innovazione and Bocconi News.
Publications
Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution
March, 2024
Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have …
Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions
March, 2024
Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language …
Classist Tools: Social Class Correlates with Performance in NLP
March, 2024
Since the foundational work of William Labov on the social stratification of language (Labov, 1964), linguistics has made concentrated …
The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising 'Alignment' in Large Language Models
November, 2023
In this paper, we address the concept of ‘alignment’ in large language models (LLMs) through the lens of post-structuralist …
SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models
November, 2023
The past year has seen rapid acceleration in the development of large language models (LLMs). For many tasks, there is now a wide range …
XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models
October, 2023
Without proper safeguards, large language models will readily follow malicious instructions and generate toxic content. This risk …
The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values
October, 2023
Human feedback is increasingly used to steer the behaviours of Large Language Models (LLMs). However, it is unclear how to collect and …
Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions
September, 2023
Training large language models to follow instructions makes them perform better on a wide range of tasks, generally becoming more …
Leveraging Label Variation in Large Language Models for Zero-Shot TextClassification
July, 2023
The zero-shot learning capabilities of large language models (LLMs) make them ideal for text classification without annotation or …
MilaNLP at SemEval-2023 Task 10: Ensembling Domain-Adapted and Regularized Pretrained Language Models for Robust Sexism Detection
July, 2023
We present the system proposed by the MilaNLP team for the Explainable Detection of Online Sexism (EDOS) shared task. We propose an …
Respectful or Toxic? Using Zero-Shot Learning with Language Models to Detect Hate Speech
July, 2023
Hate speech detection faces two significant challenges: 1) the limited availability of labeled data and 2) the high variability of hate …
Temporal and Second Language Influence on Intra-Annotator Agreement and Stability in Hate Speech Labelling
July, 2023
Much work in natural language processing (NLP) relies on human annotation. The majority of this implicitly assumes that annotator’s …
The Ecological Fallacy in Annotation: Modeling Human Label Variation goes beyond Sociodemographics
July, 2023
Many NLP tasks exhibit human label variation, where different annotators give different labels to the same texts. This variation is …
The State of Profanity Obfuscation in Natural Language Processing Scientific Publications
July, 2023
Work on hate speech has made considering rude and harmful examples in scientific publications inevitable. This situation raises various …
What about ''em''? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns
July, 2023
As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. …
Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers
May, 2023
Demographic factors (e.g., gender or age) shape our language. Previous work showed that incorporating demographic factors can …
Know Your Audience: Do LLMs Adapt to Different Age and Education Levels?
April, 2023
Large language models (LLMs) offer a range of new possibilities, including adapting the text to different audiences and their reading …
Viewpoint: Artificial Intelligence Accidents Waiting to Happen?
January, 2023
Artificial Intelligence (AI) is at a crucial point in its development: stable enough to be used in production systems, and increasingly …
Twitter-Demographer: A Flow-based Tool to Enrich Twitter Data
December, 2022
Twitter data have become essential to Natural Language Processing (NLP) and social science research, driving various scientific …
Bridging Fairness and Environmental Sustainability in Natural Language Processing
December, 2022
Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence. …
SocioProbe: What, When, and Where Language Models Learn about Sociodemographics
December, 2022
Pre-trained language models (PLMs) have outperformed other NLP models on a wide range of tasks. Opting for a more thorough …
Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages
October, 2022
Hate speech is a global phenomenon, but most hate speech datasets so far focus on English-language content. This hinders the …
Is It Worth the (Environmental) Cost? Limited Evidence for the Benefits of Diachronic Continuous Training
October, 2022
Language is constantly changing and evolving, leaving language models to quickly become outdated, both factually and linguistically. …
Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender
October, 2022
The world of pronouns is changing – from a closed word class with few members to an open set of terms to reflect identities. However, …
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design
September, 2022
Over the last several years, end-to-end neural conversational agents have vastly improved their ability to carry unrestricted, …
Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa
May, 2022
Natural Language Processing (NLP) ‘s applied nature makes it necessary to select the most effective and robust models. Producing …
Benchmarking Post-Hoc Interpretability Approaches for Transformer-based Misogyny Detection
April, 2022
Transformer-based Natural Language Processing models have become the standard for hate speech detection. However, the unconscious use …
Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals
April, 2022
Current language technology is ubiquitous and directly influences individuals' lives worldwide. Given the recent trend in AI on …
Pipelines for Social Bias Testing of Large Language Models
April, 2022
The maturity level of language models is now at a stage in which many companies rely on them to solve various tasks. However, while …
Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks
April, 2022
Labelled data is the foundation of most natural language processing tasks. However, labelling data is difficult and there often are …
XLM-EMO: Multilingual Emotion Prediction in Social Media Text
April, 2022
Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, …
Fair and Argumentative Language Modeling for Computational Argumentation
April, 2022
Although much work in NLP has focused on measuring and mitigating stereotypical bias in semantic spaces, research addressing bias in …
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists
March, 2022
Natural Language Processing (NLP) models risk overfitting to specific terms in the training data, thereby reducing their performance, …
SAFETYKIT: First Aid for Measuring Safety in Open-domain Conversational Systems
March, 2022
The social impact of natural language processing and its applications has received increasing attention. In this position paper, we …
Five sources of bias in natural language processing
August, 2021
Recently, there has been an increased interest in demographically grounded bias in natural language processing (NLP) applications. Much …
Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection
August, 2021
Reducing and counter-acting hate speech on Social Media is a significant concern. Most of the proposed automatic methods are conducted …
HONEST: Measuring Hurtful Sentence Completion in Language Models
June, 2021
Language models have revolutionized the field of NLP. However, language models capture and proliferate hurtful stereotypes, especially …
The Importance of Modeling Social Factors of Language: Theory and Practice
June, 2021
Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) …
