Philomène Chagniot

Alexander H. Liu, Kartik Khandelwal, Sandeep Subramanian et al.

We introduce the Ministral 3 series, a family of parameter-efficient dense language models designed for compute and memory constrained applications, available in three model sizes: 3B, 8B, and 14B parameters. For each model size, we release three variants: a pretrained base model for general-purpose use, an instruction finetuned, and a reasoning model for complex problem-solving. In addition, we present our recipe to derive the Ministral 3 models through Cascade Distillation, an iterative pruning and continued training with distillation technique. Each model comes with image understanding capabilities, all under the Apache 2.0 license.

Constance Ferragu, Philomene Chagniot, Vincent Coyette

In recent literature, few-shot classification has predominantly been defined by the N-way k-shot meta-learning problem. Models designed for this purpose are usually trained to excel on standard benchmarks following a restricted setup, excluding the use of external data. Given the recent advancements in large language and vision models, a question naturally arises: can these models directly perform well on meta-few-shot learning benchmarks? Multimodal foundation models like CLIP, which learn a joint (image, text) embedding, are of particular interest. Indeed, multimodal training has proven to enhance model robustness, especially regarding ambiguities, a limitation frequently observed in the few-shot setup. This study demonstrates that combining modalities from CLIP's text and image encoders outperforms state-of-the-art meta-few-shot learners on widely adopted benchmarks, all without additional training. Our results confirm the potential and robustness of multimodal foundation models like CLIP and serve as a baseline for existing and future approaches leveraging such models.

Philomène Chagniot, Flavian Vasile, David Rohde

Recommender systems are often optimised for short-term reward: a recommendation is considered successful if a reward (e.g. a click) can be observed immediately after the recommendation. The advantage of this framework is that with some reasonable (although questionable) assumptions, it allows familiar supervised learning tools to be used for the recommendation task. However, it means that long-term business metrics, e.g. sales or retention are ignored. In this paper we introduce a framework for modeling long-term rewards in the RecoGym simulation environment. We use this newly introduced functionality to showcase problems introduced by the last-click attribution scheme in the case of conversion-optimized recommendations and propose a simple extension that leads to state-of-the-art results.