85 citations · 203 across the 27 of their papers we have counts for
6 papers · 1 filter
Language Model Decoding as Likelihood-Utility Alignment
Martin Josifoski, Maxime Peyrard, Frano Rajic +8
A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm re…
Distribution inference risks: Identifying and mitigating sources of leakage
Valentin Hartmann, Léo Meynent, Maxime Peyrard +3
A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inferen…
The Glass Ceiling of Automatic Evaluation in Natural Language Generation
Pierre Colombo, Maxime Peyrard, Nathan Noiry +2
Automatic evaluation metrics capable of replacing human judgments are critical to allowing fast development of new methods. Thus, numerous research efforts have focused on crafting…
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning
Damien Teney, Maxime Peyrard, Ehsan Abbasnejad
Machine learning (ML) models are typically optimized for their accuracy on a given dataset. However, this predictive criterion rarely captures all desirable properties of a model,…
Descartes: Generating Short Descriptions of Wikipedia Articles
Marija Sakota, Maxime Peyrard, Robert West
Wikipedia is one of the richest knowledge sources on the Web today. In order to facilitate navigating, searching, and maintaining its content, Wikipedia's guidelines state that all…
On the Context-Free Ambiguity of Emoji
Justyna Czestochowska, Kristina Gligoric, Maxime Peyrard +6
Emojis come with prepacked semantics making them great candidates to create new forms of more accessible communications. Yet, little is known about how much of this emojis semantic…