Lexical Prompt Compression for Large Language Models: A Training-Free, Deterministic Pipeline with Empirical Pareto Analysis Across Eleven Task Categories
A training-free method to boost LLM efficiency by distilling complex prompts into essential token sequences.
Recent advances in large language models (LLMs) have made prompts increasingly large and complex. Techniques such as chain-of-thought reasoning (Wei et al., 2022) and in-context learning (Brown et al., 2020) frequently push real-world prompts past several thousand tokens, increas...
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Lexical prompt compression offers a vital mechanism to reduce operational costs and latency by distilling complex inputs into their most essential semantic components. By removing the need for additional training, this deterministic approach enables developers to optimize large-scale LLM deployments without sacrificing performance or reasoning capabilities.
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