  This paper focuses on task-agnostic prompt compression for better
generalizability and efficiency. Considering the redundancy in natural
language, existing approaches compress prompts by removing tokens or lexical
units according to their information entropy obtained from a causal language
model such as LLaMa-7B. The challenge is that information entropy may be a
suboptimal compression metric: (i) it only leverages unidirectional context and
may fail to capture all essential information needed for prompt compression;
(ii) it is not aligned with the prompt compression objective.
  To address these issues, we propose a data distillation procedure to derive
knowledge from an LLM to compress prompts without losing crucial information,
and meantime, introduce an extractive text compression dataset. We formulate
prompt compression as a token classification problem to guarantee the
faithfulness of the compressed prompt to the original one, and use a
Transformer encoder as the base architecture to capture all essential
information for prompt compression from the full bidirectional context. Our
approach leads to lower latency by explicitly learning the compression
objective with smaller models such as XLM-RoBERTa-large and mBERT.
  We evaluate our method on both in-domain and out-of-domain datasets,
including MeetingBank, LongBench, ZeroScrolls, GSM8K, and BBH. Despite its
small size, our model shows significant performance gains over strong baselines
and demonstrates robust generalization ability across different LLMs.
Additionally, our model is 3x-6x faster than existing prompt compression
methods, while accelerating the end-to-end latency by 1.6x-2.9x with
compression ratios of 2x-5x.
