8 papers
Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning
Sungjun Lim, Adrian Robert Minut, Nico Carlsen Daheim +3
Multi-sample reasoning measures candidate coverage, the probability that at least one of candidates is correct. However, minimizing the average loss of individual attempts does…
Geometry-Adaptive Explainer for Faithful Dictionary-Based Interpretability under Distribution Shift
Sungjun Lim, Heedong Kim, Andrew Lee +1
Mechanistic interpretability aims to explain a model's behavior by identifying causally responsible internal structures. Dictionary-based explainers such as sparse autoencoders and…
Semi-Supervised Preference Optimization with Limited Feedback
Seonggyun Lee, Sungjun Lim, Seojin Park +2
The field of preference optimization has made outstanding contributions to the alignment of language models with human preferences. Despite these advancements, recent methods still…
Eigen-Value: Efficient Domain-Robust Data Valuation via Eigenvalue-Based Approach
Youngjun Choi, Joonseong Kang, Sungjun Lim +1
Data valuation has become central in the era of data-centric AI. It drives efficient training pipelines and enables objective pricing in data markets by assigning a numeric value t…
Beyond Hard Sharing: Efficient Multi-Task Speech-to-Text Modeling with Supervised Mixture of Experts
Hojun Jin, Eunsoo Hong, Ziwon Hyung +3
Hard-parameter sharing is a common strategy to train a single model jointly across diverse tasks. However, this often leads to task interference, impeding overall model performance…
Uncertainty-driven Embedding Convolution
Sungjun Lim, Kangjun Noh, Youngjun Choi +2
Text embeddings are essential components in modern NLP pipelines. Although numerous embedding models have been proposed, no single model consistently dominates across domains and t…
Enhancing Retrieval-Augmented Audio Captioning with Generation-Assisted Multimodal Querying and Progressive Learning
Choi Changin, Lim Sungjun, Rhee Wonjong
Retrieval-augmented generation can improve audio captioning by incorporating relevant audio-text pairs from a knowledge base. Existing methods typically rely solely on the input au…
Flat Posterior Does Matter For Bayesian Model Averaging
Sungjun Lim, Jeyoon Yeom, Sooyon Kim +5
Bayesian neural networks (BNNs) estimate the posterior distribution of model parameters and utilize posterior samples for Bayesian Model Averaging (BMA) in prediction. However, des…