Closing the Social-Semantic Gap: SPSD for Edge-Based Prompt Compression in Cloud LLM Inference
A new paper introduces SPSD (Sentiment Preserving Semantic Distillation), an edge-based pipeline that compresses user prompts by removing social scaffolding before sending them to a cloud LLM, reducing input tokens by an average of 99.9 per call while maintaining response quality within a non-inferiority margin. The approach aims to cut cloud energy costs.
[2606.19364] Closing the Social-Semantic Gap: SPSD for Edge-Based Prompt Compression in Cloud LLM Inference
[Submitted on 10 Jun 2026]
Title:Closing the Social-Semantic Gap: SPSD for Edge-Based Prompt Compression in Cloud LLM Inference
View a PDF of the paper titled Closing the Social-Semantic Gap: SPSD for Edge-Based Prompt Compression in Cloud LLM Inference, by Abhinit Sen and 2 other authors
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Abstract:The prefill stage of Large Language Model (LLM) inference is a growing contributor to cloud-scale energy cost. Many consumer-support and conversational prompts contain social scaffolding: politeness markers, apologetic preamble, repetition, and rapport-building language that is important for human communication but carries low marginal information for machine reasoning. We call this discrepancy the Social-Semantic Gap. We present SPSD (Sentiment Preserving Semantic Distillation), an edge-based pipeline that compresses user prompts using a 4-bit quantised Small Language Model before transmission to a cloud-deployed LLM. Evaluation on a 248-prompt corpus using Gemma-2-2B-Instruct (Q4_K_M) as the SLM and Llama-3.1-8B-Instruct as the cloud evaluation model yields a mean input token saving of 99.9 tokens per distilled call, with all 146 distilled calls yielding positive savings. Response quality, assessed by blind LLM-as-judge scoring across 121 pairs, is non-inferior to the raw path within a pre-specified 1-point margin on a 15-point rubric; the judge awarded 43 percent ties, 28 percent distilled wins, and 29 percent raw wins. Cosine similarity is mixed: mean 0.682, median 0.712, with 54.1 percent of pairs above the 0.70 reference threshold. Safety-critical domains are conservatively routed to passthrough via rule-based gates. Per-call net energy saving is estimated at 70-270 uWh under stated assumptions. SPSD shows that on-device prompt distillation can reduce cloud LLM input-token cost while preserving response quality within a practical non-inferiority margin.
Comments: 19 pages, 7 tables, 1 figure, includes appendix
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2606.19364 [cs.LG]
(or arXiv:2606.19364v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.19364
arXiv-issued DOI via DataCite
Submission history
From: Abhinit Sen [view email] [v1] Wed, 10 Jun 2026 09:13:59 UTC (484 KB)
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