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Found 8 papers, 1 papers with code
Magistral
We introduce Magistral, Mistral's first reasoning model and our own scalable reinforcement learning (RL) pipeline.
Statistical Deficiency for Task Inclusion Estimation
Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models.
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews
In this paper, we introduce \sys, a summarization method designed to offer a concise yet comprehensive overview of scholarly reviews.
When is an Embedding Model More Promising than Another?
Embedders play a central role in machine learning, projecting any object into numerical representations that can, in turn, be leveraged to perform various downstream tasks.
$\texttt{COSMIC}$: Mutual Information for Task-Agnostic Summarization Evaluation
Assessing the quality of summarizers poses significant challenges.
Unsupervised Layer-wise Score Aggregation for Textual OOD Detection
More importantly, we show that the usual choice (the last layer) is rarely the best one for OOD detection and that far better results could be achieved if the best layer were picked.
Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data
In this work, we focus on leveraging soft-probabilities in a black-box framework, i. e. we can access the soft-predictions but not the internal states of the model.
