7 citations · 10 across the 6 of their papers we have counts for
8 papers
Where Should a Document Live: Context, Representations, or Parameters?
Nathanaël Carraz Rakotonirina, Momchil Hardalov, Gonzalo Iglesias +1
To answer questions outside of their pre-training data, large language models (LLMs) need access to new information, which can be presented in the context window as documents, enco…
Cartridges at Scale: Training Modular KV Caches over Large Document Collections
Momchil Hardalov, Gonzalo Iglesias, Adrià de Gispert
Large Language Models can reason over long contexts, yet prefilling millions of tokens is wasteful as much of the content remains static across queries. Cartridges address this by…
Benchmarking Deflection and Hallucination in Large Vision-Language Models
Nicholas Moratelli, Christopher Davis, Leonardo F. R. Ribeiro +2
Large Vision-Language Models (LVLMs) increasingly rely on retrieval to answer knowledge-intensive multimodal questions. Existing benchmarks overlook conflicts between visual and te…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
Neural Machine Translation Decoding with Terminology Constraints
Eva Hasler, Adrià De Gispert, Gonzalo Iglesias +1
Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology constra…
Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment
Gonzalo Iglesias, William Tambellini, Adrià De Gispert +2
We describe a batched beam decoding algorithm for NMT with LMBR n-gram posteriors, showing that LMBR techniques still yield gains on top of the best recently reported results with…
Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation
Felix Stahlberg, Danielle Saunders, Gonzalo Iglesias +1
SGNMT is a decoding platform for machine translation which allows paring various modern neural models of translation with different kinds of constraints and symbolic models. In thi…
Speed-Constrained Tuning for Statistical Machine Translation Using Bayesian Optimization
Daniel Beck, Adrià de Gispert, Gonzalo Iglesias +2
We address the problem of automatically finding the parameters of a statistical machine translation system that maximize BLEU scores while ensuring that decoding speed exceeds a mi…