PoC: Performance-oriented Context Compression for Large Language Models via Performance Prediction
While context compression can mitigate the growing inference costs of Large Language Models (LLMs) by shortening contexts, existing methods that specify a target compression ratio or length suffer from unpredictable performance degradation, hindering their reliable deployment. We introduce a paradigm shift to Performance-oriented Context Compression (PoC), where developers specify an acceptable performance floor instead of a compression ratio. PoC employs a lightweight performance predictor to automatically find the most aggressive compression ratio that satisfies this constraint before steering an off-the-shelf compressor. We design and compare two predictor variants: a simple context-ag...
| Authors | Runsong Zhao, Shilei Liu, Jiwei Tang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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