FocusLLM: Precise Understanding of Long Context by Dynamic Condensing
This addresses the challenge of handling long contexts efficiently for LLM applications, offering a versatile solution with reduced resource requirements.
The paper tackles the problem of enabling LLMs to precisely understand long contexts by introducing FocusLLM, a framework that dynamically condenses information from long sequences into manageable chunks, achieving superior performance on downstream tasks with up to 400K tokens while using less training cost than previous methods.
Empowering LLMs with the ability to precisely understand long contexts is crucial for many downstream applications. However, handling long contexts with conventional transformer architecture requires substantial training and inference resources. Existing context condensing methods cannot accurately understand the full context, as there is a considerable amount of information loss in the condensing process. To address these issues, we present FocusLLM, a framework designed to extend the fixed context length of any decoder-only LLM, allowing the model to focus on relevant information from very long sequences. FocusLLM first divides long text input into chunks based on the model's original context length. It then employs the dynamic condensing process to distill crucial information from each chunk. Ultimately, through the novel parallel decoding mechanism, FocusLLM can integrate the extracted information into its local context. FocusLLM stands out for great training efficiency and versatility: trained with an 8K input length and with much less training cost than previous methods, FocusLLM exhibits superior performance across downstream 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.