732 citations · 1.7k across the 44 of their papers we have counts for
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A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
Minghui Chen, Hrad Ghoukasian, Ruinan Jin +3
Federated Learning (FL) enables decentralized, privacy-preserving model training but struggles to balance global generalization and local personalization due to non-identical data…
ContextLeak: Auditing Leakage in Private In-Context Learning Methods
Jacob Choi, Shuying Cao, Xingjian Dong +4
In-Context Learning (ICL) has become a standard technique for adapting Large Language Models (LLMs) to specialized tasks by supplying task-specific exemplars within the prompt. How…
Beyond URLs: Metadata Diversity and Position for Efficient LLM Pretraining
Dongyang Fan, Diba Hashemi, Sai Praneeth Karimireddy +1
Incorporating metadata in Large Language Models (LLMs) pretraining has recently emerged as a promising approach to accelerate training. However prior work highlighted only one usef…
f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness
Subhodip Panda, Dhruv Tarsadiya, Shashwat Sourav +2
Influence estimation methods promise to explain and debug machine learning by estimating the impact of individual samples on the final model. Yet, existing methods collapse under t…
Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness
Amin Banayeeanzade, Ala N. Tak, Fatemeh Bahrani +5
The ability to control LLMs' emulated emotional states and personality traits is an essential step in enabling rich, human-centered interactions in socially interactive settings. W…
Conformal Prediction Adaptive to Unknown Subpopulation Shifts
Nien-Shao Wang, Duygu Nur Yaldiz, Yavuz Faruk Bakman +1
Conformal prediction is widely used to equip black-box machine learning models with uncertainty quantification, offering formal coverage guarantees under exchangeable data. However…
Reconsidering LLM Uncertainty Estimation Methods in the Wild
Yavuz Bakman, Duygu Nur Yaldiz, Sungmin Kang +4
Large Language Model (LLM) Uncertainty Estimation (UE) methods have become a crucial tool for detecting hallucinations in recent years. While numerous UE methods have been proposed…
A Systematic Analysis of Base Model Choice for Reward Modeling
Kian Ahrabian, Pegah Jandaghi, Negar Mokhberian +2
Reinforcement learning from human feedback (RLHF) and, at its core, reward modeling have become a crucial part of training powerful large language models (LLMs). One commonly overl…
Do Data Valuations Make Good Data Prices?
Dongyang Fan, Tyler J. Rotello, Sai Praneeth Karimireddy
As large language models increasingly rely on external data sources, compensating data contributors has become a central concern. But how should these payments be devised? We revis…