Meta AI’s Long-Context LLMs: Redefining the Landscape of Natural Language Processing
Large Language Models (LLMs) have revolutionized the world of Natural Language Processing (NLP) with their remarkable abilities to handle intricate and complex tasks. These models have been trained on massive datasets using immense computational power, showcasing impressive long-context capabilities.
However, the key to accessing these long-context capabilities lies within proprietary LLM APIs, and there has been a lack of open recipes for constructing comparable long-context models that can deliver similar downstream performance. Additionally, existing open-source long-context models often fall short on evaluations, primarily relying on language modeling loss and synthetic tasks, while neglecting the need to maintain strong performance on standard short-context tasks.
In a new paper Effective Long-Context Scaling of Foundation Models, a Meta AI research team presents a series of long-context LLMs, built through the pretraining from LLAMA 2. These models support effective context windows of up to 32,768 tokens and outperform all existing open-sourced models in terms of performance.
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