Doc-to-Atom: Learning to Compile and Compose Memory Atoms
Researchers have introduced Doc-to-Atom (Doc2Atom), a new framework designed to improve how large language models handle long documents. Unlike previous methods that create a single adapter for an entire document, Doc2Atom breaks down documents into "knowledge atoms." Each atom is compiled into a small, independent adapter that can be selectively retrieved and combined at inference time. This approach aims to reduce memory usage and enhance reasoning capabilities for lengthy texts, outperforming existing Doc-to-LoRA methods in experiments. AI
IMPACT Enhances LLM efficiency and effectiveness in processing and reasoning over lengthy documents.