1 citations · 1 across the 13 of their papers we have counts for
15 papers
Disentangling Question, Response, and Verifier Languages in Multilingual P(True) Self-Evaluation
Jewon Yeom, Jaewon Sok, Seonghyeon Park +3
When a language model is asked whether its own answer is correct, three languages are involved: that of the question, that requested for the answer, and that of the verifier instru…
Where Predictive Supervision Goes Shapes What VLA Policies Learn
Hanseul Kim, Jewon Yeom, Youngjoon Jeong +2
Future prediction is increasingly used to improve vision-language-action (VLA) policies, based on the premise that anticipating scene evolution encourages representations useful fo…
Answer First, Reason Later: When Commitment Order Costs Accuracy in Diffusion Language Models
Jewon Yeom, Jaewon Sok, Seonghyeon Park +3
Masked diffusion language models revise many masked output positions in parallel. We call a token committed once it becomes visible and is never masked again, and call a response a…
What Makes Video World Model Latents Action-Relevant: Prediction over Reconstruction
Jewon Yeom, Hanseul Kim, Jeongjae Park +3
Video world models are increasingly used to provide predictive visual representations, yet it remains unclear which pretraining signals induce action-relevant structure in their la…
Hallucination as Commitment Failure: Larger LLMs Misfire Despite Knowing the Answer
Jewon Yeom, Jaewon Sok, Heejun Kim +3
Hallucination is often viewed as a direct consequence of missing knowledge: a model answers incorrectly when the correct answer is absent from its generation-time distribution, and…
From Noise to Diversity: Random Embedding Injection in LLM Reasoning
Heejun Kim, Seungpil Lee, Jewon Yeom +5
Recent soft prompt research has tried to improve reasoning by inserting trained vectors into LLM inputs, yet whether the gain comes from the learned content or from the act of inje…
Beyond Case Law: Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA
Kyubyung Chae, Jewon Yeom, Jeongjae Park +5
Legal QA benchmarks have predominantly focused on case law, overlooking the unique challenges of statute-centric regulatory reasoning. In statutory domains, relevant evidence is di…
Robust Domain Generalization under Divergent Marginal and Conditional Distributions
Jewon Yeom, Kyubyung Chae, Hyunggyu Lim +3
Domain generalization (DG) aims to learn predictive models that can generalize to unseen domains. Most existing DG approaches focus on learning domain-invariant representations und…
Efficient Epistemic Uncertainty Estimation for Large Language Models via Knowledge Distillation
Seonghyeon Park, Jewon Yeom, Jaewon Sok +3
Quantifying uncertainty in Large Language Models (LLMs) is essential for mitigating hallucinations and enabling risk-aware deployment in safety-critical tasks. However, estimating…
Garbage Attention in Large Language Models: BOS Sink Heads and Sink-aware Pruning
Jaewon Sok, Jewon Yeom, Seonghyeon Park +2
Large Language Models (LLMs) are known to contain significant redundancy, yet a systematic explanation for why certain components, particularly in higher layers, are more redundant…
EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs
Jewon Yeom, Jaewon Sok, Seonghyeon Park +2
Improving the reasoning abilities of large language models (LLMs) has largely relied on iterative self-training with model-generated data. While effective at boosting accuracy, exi…
Stable On-Policy Distillation through Adaptive Target Reformulation
Ijun Jang, Jewon Yeom, Juan Yeo +2
Knowledge distillation (KD) is a widely adopted technique for transferring knowledge from large language models to smaller student models; however, conventional supervised KD often…
Generalized and Personalized Federated Learning with Black-Box Foundation Models via Orthogonal Transformations
Eun Gyung Kong, Je Won Yeom, Yonghoon Jeon +1
Federated Learning (FL) facilitates decentralized model training while preserving data privacy. However, achieving both robust generalization and effective personalization simultan…
"Well, Keep Thinking": Enhancing LLM Reasoning with Adaptive Injection Decoding
Hyunbin Jin, Je Won Yeom, Seunghyun Bae +1
Large language models (LLMs) exhibit strong reasoning abilities, often attributed to few-shot or zero-shot chain-of-thought (CoT) prompting. While effective, these methods require…
HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models
Guijin Son, Hanwool Lee, Suwan Kim +6
Large language models (LLMs) trained on massive corpora demonstrate impressive capabilities in a wide range of tasks. While there are ongoing efforts to adapt these models to langu…