FocusLLM: Scaling LLM's Context by Parallel Decoding
Authors: name: Zhenyu Li, name: Yike Zhang, name: Tengyu Pan, name: Yutao Sun, name: Zhichao Duan, name: Junjie Fang, name: Rong Han, name: Zixuan Wang, name: Jianyong Wang

Empowering LLMs with the ability to utilize useful information from a long
context is crucial for many downstream applications. However, achieving long
context lengths with the conventional transformer architecture requires
substantial training and inference resources. In this paper, we present
FocusLLM, a framework designed to extend the context length of any decoder-only
LLM, enabling the model to focus on relevant information from very long
sequences. FocusLLM processes long text inputs by dividing them into chunks
based on the model's original context length to alleviate the issue of
attention distraction. Then, it appends the local context to each chunk as a
prompt to extract essential information from each chunk based on a novel
parallel decoding mechanism, and ultimately integrates the extracted
information into the local context. FocusLLM stands out for great training
efficiency and versatility: trained with an 8K input length with much less
training cost than previous methods, FocusLLM exhibits superior performance
across downstream long-context tasks and maintains strong language modeling
ability when handling extensive long texts, even up to 400K tokens. Our code is
available at https://github.com/leezythu/FocusLLM.