Every Time I Hire a Linguist, Inference Costs Go Down: On Linguistic Rules as Effective Prompt Compressors
Quick Answer
This study demonstrates that linguistic rules can effectively compress prompts for LLMs, reducing inference costs without requiring LM-based scoring.
Quick Take
By employing evolutionary search over linguistic features, the proposed compressor achieves performance comparable to advanced methods, particularly under light-to-moderate compression. The findings suggest a shift from token pruning to sentence extraction as compression ratios increase.
Key Points
- Linguistic rules serve as effective prompt compressors, eliminating the need for LM forward passes.
- The compressor uses only CPU-side processing, enhancing efficiency during deployment.
- Performance is comparable to advanced prompt-compression strategies, especially under light compression.
- Effective compression combines signals across linguistic levels, adapting as compression ratios increase.
- Distinct patterns observed in Direct and Reconstruction paths during evaluation.
DeepSignal Analysis
What happened
The study explores the use of linguistic rules for prompt compression in large language models (LLMs), aiming to reduce inference costs without relying on language model scoring. The proposed method employs evolutionary search to identify effective rule combinations, achieving competitive performance compared to advanced compression techniques.
Key evidence
- The linguistic compressor developed in the study does not require language model forward passes, relying solely on CPU-side processing for compression.
- The evaluation of the compressor shows that it performs similarly to recent advanced prompt-compression strategies, particularly under light-to-moderate compression.
- As compression ratios increase, the study finds that effective compression shifts from token pruning to sentence extraction, indicating a change in strategy.
Why it matters
This research suggests a potential shift in how prompt compression is approached in LLMs, moving away from complex token selection methods that incur high computational costs. By demonstrating that linguistic rules can serve as effective compressors, it opens up new avenues for optimizing inference costs, which is crucial for deploying LLMs in resource-constrained environments.
Paper Resources
Source Excerpt
Prompt compression shortens input to reduce inference cost, yet existing methods score token importance through LM forward passes. It remains questionable whether such nuanced, costly token selection is necessary. Compression requires identifying informative content, a problem that linguistic research has long addressed through cues that can be operationalized as deterministic rules. We therefore ask: can \textbf{linguistic rules alone} serve as effective prompt compressors, without LM-based
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