Lossless Token Sequence Compression via Meta-Tokens
This provides a lossless compression method for LLM inputs, beneficial for tasks requiring strict semantic preservation, though it is incremental as it adapts existing compression techniques like LZ77.
The paper tackles the problem of compressing input token sequences for LLMs without losing semantic information, achieving average length reductions of 27% and 18% on two tasks, which translates to 47% and 33% less encoding computation due to quadratic attention.
Existing work on prompt compression for Large Language Models (LLM) focuses on lossy methods that try to maximize the retention of semantic information that is relevant to downstream tasks while significantly reducing the sequence length. In this paper, we introduce a task-agnostic lossless compression technique similar to LZ77 that makes it possible to reduce the input token sequence length on average by 27\% and 18\% for the two evaluation tasks explored here. Given that we use transformer-based LLMs, this equates to 47\% and 33\% less encoding computation, respectively, due to the quadratic nature of attention. The token sequence transformation is trivial to reverse and highlights that no semantic information is lost in the process. We evaluate our proposed approach on two tasks that require strict preservation of semantics/syntax and demonstrate that existing lossy compression methods perform poorly in this setting. We find that our lossless compression technique produces only a small gap in performance compared to using the uncompressed input and posit that larger models and an expanded computing budget would likely erase the gap entirely.