Researchers have introduced PromptGraph, a novel method for enhancing privacy in Large Language Model (LLM) inference. This approach models prompts as attributed graphs, where nodes represent individual spans with privacy scores and edges capture contextual dependencies crucial for maintaining utility. By optimizing for both privacy gain and the preservation of these contextual links, PromptGraph aims to strike a better balance between protecting sensitive information and ensuring the model's functional output compared to existing span-level sanitization techniques. AI
IMPACT Enhances privacy in LLM inference by modeling contextual dependencies, potentially improving data protection without sacrificing utility.
RANK_REASON The cluster contains a research paper detailing a new method for LLM inference. [lever_c_demoted from research: ic=1 ai=1.0]
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- alphaXiv
- arXiv
- CatalyzeX
- Connected Papers
- DagsHub
- Gotit.pub
- Hugging Face
- Large Language Model
- Litmaps
- PromptGraph
- ScienceCast
- Scite
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