565 citations · 637 across the 9 of their papers we have counts for
7 papers · 1 filter
MortalMATH: Evaluating the Conflict Between Reasoning Objectives and Emergency Contexts
Etienne Lanzeray, Stephane Meilliez, Malo Ruelle +1
Large Language Models are increasingly optimized for deep reasoning, prioritizing the correct execution of complex tasks over general conversation. We investigate whether this focu…
Recipient Profiling: Predicting Characteristics from Messages
Martin Borquez, Mikaela Keller, Michael Perrot +1
It has been shown in the field of Author Profiling that texts may inadvertently reveal sensitive information about their authors, such as gender or age. This raises important priva…
Consent in Crisis: The Rapid Decline of the AI Data Commons
Shayne Longpre, Robert Mahari, Ariel Lee +46
General-purpose artificial intelligence (AI) systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we…
The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI
Shayne Longpre, Robert Mahari, Anthony Chen +14
The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To reme…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Zero-Shot Recommendation as Language Modeling
Damien Sileo, Wout Vossen, Robbe Raymaekers
Recommendation is the task of ranking items (e.g. movies or products) according to individual user needs. Current systems rely on collaborative filtering and content-based techniqu…
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…