1 citations · 2 across the 8 of their papers we have counts for
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Small Encoders Can Rival Large Decoders in Detecting Groundedness
Istabrak Abbes, Gabriele Prato, Quentin Fournier +4
Augmenting large language models (LLMs) with external context significantly improves their performance in natural language processing (NLP) tasks. However, LLMs struggle to answer…
NeoBERT: A Next-Generation BERT
Lola Le Breton, Quentin Fournier, Mariam El Mezouar +2
Recent innovations in architecture, pre-training, and fine-tuning have led to the remarkable in-context learning and reasoning abilities of large auto-regressive language models su…
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
Megh Thakkar, Quentin Fournier, Matthew D Riemer +4
Large language models are first pre-trained on trillions of tokens and then instruction-tuned or aligned to specific preferences. While pre-training remains out of reach for most r…