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Lexical Prompt Compression for Large Language Models: A Training-Free, Deterministic Pipeline with Empirical Pareto Analysis Across Eleven Task Categories
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prompt engineeringcompressionNLPdeterministic methods
2609.13154
Builder Relevance
2h ago80%
Abstract
This paper explores a training-free, deterministic pipeline for compressing prompts for large language models, achieving significant token reduction while maintaining output quality.
Reality Card
Core Claim
The most aggressive configuration of the proposed pipeline achieves a mean token reduction of 40.3% with a BERTScore-F1 of 0.876.
Method / Result
40.3% mean token reduction at BERTScore-F1 of 0.876.
Limitations
The study indicates commonsense reasoning as a systematic failure mode under aggressive compression, which may affect generalizability.
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