Latest Posts

This blog post delves into how scaling laws apply to Large Language Models (LLMs) in machine translation. We look at a recent study on the impact of dataset size on the translation quality of the Llama 3 8B model, focusing on how much data is needed to fine-tune LLMs for specific tasks.

This blog post explores the impact of Generative Pre-trained Transformers (GPT) on Machine Translation (MT). With advances seen in models like GPT-4, Large Language Models (LLMs) are now leading the way in translating creative texts, addressing challenges like stylistic nuances and cultural connotations. We'll discuss how these models have advanced MT, the remaining challenges, and the promising approaches that might help bridge the creativity gap in translation.

This blog post explores the impact of Generative Pre-trained Transformers (GPT) on Machine Translation (MT). With advances seen in models like GPT-4, Large Language Models (LLMs) are now leading the way in translating creative texts, addressing challenges like stylistic nuances and cultural connotations. We'll discuss how these models have advanced MT, the remaining challenges, and the promising approaches that might help bridge the creativity gap in translation.

Large Language Models (LLMs) are advanced artificial intelligence systems that generate text by predicting subsequent tokens based on the context of previous ones, utilizing a structure known as the Transformers architecture. These models are trained on vast datasets, enabling them to understand and produce language that can adapt to a variety of tasks and contexts.

This blog post introduces to the latest advancement in machine translation: Neural Machine Translation (NMT). It uses deep learning through neural networks to improve translation accuracy and context-awareness, replacing older statistical models and becoming in 2017 the de-facto standard due to its superior performance.