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MemLong utilizes an external retriever for retrieving historical information which enhances the capabilities of long-context LLMs. It consistently outperforms other SoTA LLMs on long-context benchmarks and can extend the context length on a single 3090 GPU from 4k up to 80k. https://t.co/FmeSv9tMSB
Long-context LLMs Struggle with Long In-context Learning https://t.co/VRKdECKOIu #longiclbench #learning #long
How a research scientist at Google DeepMind use LLMs Really cool read about the practical things LLMs do rly well: https://t.co/vVBnUm5xXM https://t.co/7u5J3cJlwB
Recent advancements in long-context language models (LLMs) have been highlighted, focusing on improving their ability to handle extended contexts. MemLong, a memory-augmented retrieval system, enhances LLMs by using an external retriever and a memory bank, significantly extending context length from 4,000 to 80,000 tokens. This development allows LLMs to outperform other state-of-the-art models on long-context benchmarks, even on a single 3090 GPU. Additionally, new methods such as Writing in the Margins, ReMamba, Dolphin, FocusLLM, and LongRAG have been introduced to improve the efficiency of processing long contexts in LLMs.
