UIO-LLMs: Unbiased Incremental Optimization for Long-Context LLMs
This addresses the challenge of processing long texts for LLM users, representing a strong specific gain rather than a broad paradigm shift.
The paper tackles the problem of limited context window sizes in large language models by introducing UIO-LLMs, an unbiased incremental optimization approach that extends the context window of Llama2-7b-chat from 4K to 100K tokens with only 2% additional parameters while maintaining near-linear inference cost.
Managing long texts is challenging for large language models (LLMs) due to limited context window sizes. This study introduces UIO-LLMs, an unbiased incremental optimization approach for memory-enhanced transformers under long-context settings. We initially conceptualize the process as a streamlined encoder-decoder framework where the weights-shared encoder and decoder respectively encapsulate a context segment into memories and leverage these memories to predict outputs of the subsequent segment. Subsequently, by treating our memory-enhanced transformers as fully-connected recurrent neural networks (RNNs), we refine the training process using the Truncated Backpropagation Through Time (TBPTT) algorithm, which incorporates innovative incremental optimization techniques. These techniques not only diminish time complexity but also address the bias in gradient computation through an unbiased optimization process. UIO-LLMs successfully handle long context, such as extending the context window of Llama2-7b-chat from 4K to 100K tokens with minimal 2% additional parameters, while keeping the inference cost nearly linear as context length increases.