SCOPE: A Generative Approach for LLM Prompt Compression
Quick summary
arXiv:2508.15813v2 Announce Type: replace-cross Abstract: A big issue in modern LLM applications is they tend to feed long context to LLM, which results in high inference cost and latency, and may exceed the context limit. Prompt compression addresses this issue by reducing the length of input context with minimum loss of generation quality, i.e, the goal of prompt compression is to shorten the LLM input while maintaining a high generation quality. To overcome these limitations, we propose SCOPE, a training-free generative prompt compression framework based on chunk-level rewriting. Unlike the
Key takeaways
- arXiv:2508.15813v2 Announce Type: replace-cross Abstract: A big issue in modern LLM applications is they tend to feed long context to LLM, which results in high inference cost and latency, and may exceed the context limit.
- Prompt compression addresses this issue by reducing the length of input context with minimum loss of generation quality, i.e, the goal of prompt compression is to shorten the LLM input while maintaining a high generation quality.
- To overcome these limitations, we propose SCOPE, a training-free generative prompt compression framework based on chunk-level rewriting.
Why it matters
The importance of “SCOPE: A Generative Approach for LLM Prompt Compression” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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