65 citations · 176 across the 25 of their papers we have counts for
8 papers · 1 filter
Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation
Nuno M. Guerreiro, Pierre Colombo, Pablo Piantanida +1
Neural machine translation (NMT) has become the de-facto standard in real-world machine translation applications. However, NMT models can unpredictably produce severely pathologica…
Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data
Maxime Darrin, Pablo Piantanida, Pierre Colombo
Implementing effective control mechanisms to ensure the proper functioning and security of deployed NLP models, from translation to chatbots, is essential. A key ingredient to ensu…
Beyond Mahalanobis-Based Scores for Textual OOD Detection
Pierre Colombo, Eduardo D. C. Gomes, Guillaume Staerman +2
Deep learning methods have boosted the adoption of NLP systems in real-life applications. However, they turn out to be vulnerable to distribution shifts over time which may cause s…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation
Cyril Chhun, Pierre Colombo, Chloé Clavel +1
Research on Automatic Story Generation (ASG) relies heavily on human and automatic evaluation. However, there is no consensus on which human evaluation criteria to use, and no anal…
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…
Learning Disentangled Textual Representations via Statistical Measures of Similarity
Pierre Colombo, Guillaume Staerman, Nathan Noiry +1
When working with textual data, a natural application of disentangled representations is fair classification where the goal is to make predictions without being biased (or influenc…
A Differential Entropy Estimator for Training Neural Networks
Georg Pichler, Pierre Colombo, Malik Boudiaf +2
Mutual Information (MI) has been widely used as a loss regularizer for training neural networks. This has been particularly effective when learn disentangled or compressed represen…