Three New Studies Examine Privacy and Research Applications of Large Language Models

Three New Studies Examine Privacy and Research Applications of Large Language Models

Three recent arXiv preprints address different aspects of large language model deployment and research:

Privacy in Knowledge-Enhanced LLMs: A December 2024 paper (arXiv:2512.03100v1) examines privacy risks in Retrieval-Augmented Generation (RAG) and Supervised Finetuning (SFT) systems, specifically regarding membership inference attacks against knowledge-intensive LLMs. The authors propose an “Ensemble Privacy Defense” approach, according to the abstract.

LLMs for Econometric Research: Another study (arXiv:2412.07031v3, updated January 2025) presents “an econometric framework” for using LLMs in economic research. According to the abstract, the paper addresses how “LLMs enable researchers to analyze text at unprecedented scale and minimal cost,” allowing researchers to “revisit old questions and tackle novel ones with rich data.”

KV-Cache Privacy Risks: A January 2025 paper (arXiv:2508.09442v2) titled “Shadow in the Cache” investigates privacy vulnerabilities in the Key-Value (KV) cache mechanism used to accelerate LLM inference. The KV cache “stores intermediate attention computations (Key and Value pairs) to avoid redundant calculations,” according to the abstract, which describes it as “a fundamental mechanism for accelerating Large Language Model (LLM) inference.”

All three papers are cross-posted to the cs.AI category on arXiv.