3 citations · 4 across the 11 of their papers we have counts for
11 papers
Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling
Falcon LLM Team, Iheb Chaabane, Puneesh Khanna +8
This work introduces Falcon-H1R, a 7B-parameter reasoning-optimized model that establishes the feasibility of achieving competitive reasoning performance with small language models…
Alignment with Preference Optimization Is All You Need for LLM Safety
Reda Alami, Ali Khalifa Almansoori, Ahmed Alzubaidi +3
We demonstrate that preference optimization methods can effectively enhance LLM safety. Applying various alignment techniques to the Falcon 11B model using safety datasets, we achi…
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse
Mohamed El Amine Seddik, Suei-Wen Chen, Soufiane Hayou +2
The phenomenon of model collapse, introduced in (Shumailov et al., 2023), refers to the deterioration in performance that occurs when new models are trained on synthetic data gener…
Investigating Regularization of Self-Play Language Models
Reda Alami, Abdalgader Abubaker, Mastane Achab +2
This paper explores the effects of various forms of regularization in the context of language model alignment via self-play. While both reinforcement learning from human feedback (…
Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model
Hugo Lebeau, Mohamed El Amine Seddik, José Henrique de Morais Goulart
We study the estimation of a planted signal hidden in a recently introduced nested matrix-tensor model, which is an extension of the classical spiked rank-one tensor model, motivat…
Do Vision and Language Encoders Represent the World Similarly?
Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali +4
Aligned text-image encoders such as CLIP have become the de facto model for vision-language tasks. Furthermore, modality-specific encoders achieve impressive performances in their…