11 papers
From Drift to Coherence: Stabilizing Beliefs in LLMs
SongEun Kim, Seungyoo Lee, Edwin Fong +2
Large language models (LLMs) are often hypothesized to perform implicit Bayesian inference, yet a key coherence condition, the martingale property of predictive beliefs, has been s…
Parallel Test-Time Scaling with Multi-Sequence Verifiers
Yegon Kim, Seungyoo Lee, Chaeyun Jang +2
Parallel test-time scaling, which generates multiple candidate solutions for a single problem, is a powerful technique for improving large language model performance. However, it i…
Compact Memory for Continual Logistic Regression
Yohan Jung, Hyungi Lee, Wenlong Chen +4
Despite recent progress, continual learning still does not match the performance of batch training. To avoid catastrophic forgetting, we need to build compact memory of essential p…
Reliable Decision Making via Calibration Oriented Retrieval Augmented Generation
Chaeyun Jang, Deukhwan Cho, Seanie Lee +2
Recently, Large Language Models (LLMs) have been increasingly used to support various decision-making tasks, assisting humans in making informed decisions. However, when LLMs confi…
Verbalized Confidence Triggers Self-Verification: Emergent Behavior Without Explicit Reasoning Supervision
Chaeyun Jang, Moonseok Choi, Yegon Kim +2
Uncertainty calibration is essential for the safe deployment of large language models (LLMs), particularly when users rely on verbalized confidence estimates. While prior work has…
Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain
Jungwon Choi, Hyungi Lee, Byung-Hoon Kim +1
Graph Neural Networks (GNNs) have shown promise in learning dynamic functional connectivity for distinguishing phenotypes from human brain networks. However, obtaining extensive la…