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prompt compression

topic2 events
papersTODAY 04:00 UTC

arXiv Paper Proposes Training-Free Lexical Prompt Compression for LLMs

A new arXiv preprint describes a deterministic, training-free pipeline for shortening the prompts given to large language models by compressing their lexical content. The authors report a Pareto analysis of the trade-offs between compression and task performance across eleven task categories. The work targets the growing cost and context limits caused by long prompts in techniques like chain-of-thought and in-context learning.

prompt-compression/llm/efficiency/arxiv
papersTODAY 04:00 UTC

arXiv Paper Proposes Graph-Based Prompt Compression Using Lévy Walk Pruning

A new arXiv preprint argues that current prompt compression techniques treat text as a flat token sequence and therefore miss how key information is scattered across a document and linked by syntactic and semantic ties. The authors instead model the text as a multiplex graph and prune it with a Lévy walk-guided procedure to decide which parts to keep. The work is listed as a cross-listing replacement on cs.AI.

prompt-compression/research/graphs/llm