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20232026
most citedFollow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems

4 citations · 5 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions

Zhenting Qi, Huangyuan Su, Ao Qu +13

How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedrich Hayek's economic theory of d…

cs.CL2026

Self-Improving Language Models with Bidirectional Evolutionary Search

Guowei Xu, Zhenting Qi, Huangyuan Su +4

Search has been proposed as an effective method for self-improving language models and agentic systems, both for post-training sample generation and for inference. However, widely…

cs.CL2025

EvoLM: In Search of Lost Language Model Training Dynamics

Zhenting Qi, Fan Nie, Alexandre Alahi +6

Modern language model (LM) training has been divided into multiple stages, making it difficult for downstream developers to evaluate the impact of design choices made at each stage…

cs.CL2024

Quantifying Generalization Complexity for Large Language Models

Zhenting Qi, Hongyin Luo, Xuliang Huang +5

While large language models (LLMs) have shown exceptional capabilities in understanding complex queries and performing sophisticated tasks, their generalization abilities are often…

cs.CL2024★ 4 cited

Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems

Zhenting Qi, Hanlin Zhang, Eric Xing +2

Retrieval-Augmented Generation (RAG) improves pre-trained models by incorporating external knowledge at test time to enable customized adaptation. We study the risk of datastore le…

cs.CL2023★ 1 cited

A Study on the Calibration of In-context Learning

Hanlin Zhang, Yi-Fan Zhang, Yaodong Yu +5

Accurate uncertainty quantification is crucial for the safe deployment of machine learning models, and prior research has demonstrated improvements in the calibration of modern lan…