Every Time I Hire a Linguist, Inference Costs Go Down (arxiv.org)

A recent study highlighted in "Every Time I Hire a Linguist, Inference Costs Go Down" proposes using linguistic rules as effective prompt compressors for language models (LMs), potentially reducing inference costs significantly. Traditional prompt compression techniques rely on costly LM forward passes to assess token importance, raising questions about their efficiency. This research posits that deterministic linguistic rules can serve as an alternative, streamlining the compression process without necessitating LM-based scoring. The study employs an offline evolutionary search to identify optimal combinations of lexical, syntactic, semantic, and discourse cues, resulting in a linguistic compressor that operates solely on CPU resources during deployment. When tested on diverse datasets such as short passages and dialogue-memory question answering, these linguistically-driven compressors demonstrated performance on par with advanced prompt-compression methods, particularly under light-to-moderate compression. As compression intensifies, the approach adapts, shifting from pruning tokens to extracting whole sentences. This innovation could lead to more efficient AI systems by reducing operational costs and improving processing times, underscoring the value of integrating linguistic insight into AI methodologies.