1 citations · 1 across the 3 of their papers we have counts for
5 papers
Reliable Self-Evolution with Imperfect Proxy Rewards
Kangjun Noh, Soyu Kim, Kyungwoo Song
Large language model (LLM)-based self-evolving search is a promising approach to scientific discovery. However, high-fidelity evaluation of every candidate is prohibitively expensi…
Range-GRPO: Policy Optimization via Pairwise Relations among Reward Intervals
Ryunyi Lee, Kangjun Noh, Somin Kim +2
As the use of large language models (LLMs) expands, post-training has become increasingly important for adapting them to downstream tasks. However, obtaining reliable supervision r…
Multi-LLM Adaptive Conformal Inference for Reliable LLM Responses
Kangjun Noh, Seongchan Lee, Ilmun Kim +1
Ensuring factuality is essential for the safe use of Large Language Models (LLMs) in high-stakes domains such as medicine and law. Conformal inference provides distribution-free gu…
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…
LBC: Language-Based-Classifier for Out-Of-Variable Generalization
Kangjun Noh, Baekryun Seong, Hoyoon Byun +3
Large Language Models (LLMs) have great success in natural language processing tasks such as response generation. However, their use in tabular data has been limited due to their i…