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Infinite Context Encoding in LLMs
One of the ultimate goals for long-context language models is the ability to precisely encode an infinite context. In practice, this refers to a model's capacity to continuously read words and handle extremely long contexts or streaming data without being limited by a fixed input window.
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Foundations of Large Language Models
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
General Applicability of Long-Context Methods
Context Scaling for LLM Performance Improvement
Model Selection for Large-Scale Document Summarization
A development team is tasked with creating a system that can analyze and answer questions about lengthy legal documents, some of which are over 100,000 words long. When selecting a foundational language model for this task, what is the most critical architectural characteristic they should prioritize to ensure the system can effectively process the entirety of these documents at once?
Evaluating System Architectures for Long-Document Q&A
Infinite Context Encoding in LLMs
Continuous-Space Attention for Infinite Context