arXiv cs.CL· Jianfei Ma, Zhaoxin Feng, Emmanuele Chersoni, Si Chen·· 3 天前AI 评分33
语言规则能否替代 LM 打分做提示词压缩？EMNLP 2026 论文提出纯 CPU 语言压缩器
Every Time I Hire a Linguist, Inference Costs Go Down: On Linguistic Rules as Effective Prompt Compressors
AI 导读
EMNLP 2026 论文提出仅靠语言规则做提示词压缩，部署时无需 LM 前向计算，压缩全程在 CPU 侧完成。该方法通过离线进化搜索组合词汇、句法、语义和篇章规则，在短文本、多文档推理和对话记忆问答数据集上取得与近期先进提示词压缩策略相近的表现。性能在轻度至中度压缩下最强，压缩越激进越退化，且规则随压缩比升高从 token 剪枝转向句子抽取。
正文 · 原文
Abstract:Prompt compression shortens LLM 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 scoring at compression time?
To address this, we conduct offline evolutionary search over lexical, syntactic, semantic, and discourse seeds to find competitive rule combinations. The resulting linguistic compressor requires no LM forward pass at deployment and uses only CPU-side processing for compression. We evaluate it with a dual-path protocol to balance compression quality and reconstruction fidelity.
Across short passages, multi-document reasoning, and dialogue-memory QA datasets, evolved compressors achieve performance similar to that of recent advanced prompt-compression strategies. Performance is strongest under light-to-moderate compression and degrades as compression becomes more aggressive, while the Direct and Reconstruction paths exhibit distinct patterns. Evolutionary analysis reveals that effective compression fuses signals across linguistic levels and, as the compression ratio increases, rules shift from token pruning to sentence extraction.
| Comments: | EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.25335 [cs.CL] |
| (or arXiv:2607.25335v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.25335 arXiv-issued DOI via DataCite |
Submission history
From: Zhaoxin Feng [view email]
[v1]
Tue, 28 Jul 2026 06:33:27 UTC (735 KB)
[v2]
Wed, 29 Jul 2026 12:56:44 UTC (735 KB)
[v3]
Wed, 14 Oct 2026 03:45:08 UTC (735 KB)
来源：arXiv cs.CL · arxiv.org