Papers by To Eun Kim
MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers (2025.emnlp-main)
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| Challenge: | Different retrievers offer distinct, often complementary signals, but they are not optimal for all queries. |
| Approach: | They propose a zero-shot, weighted combination of heterogeneous retrievers . they validate this intuition by incorporating specialized non-oracle human information sources . |
| Outcome: | Experiments show that a mixture of heterogeneous retrievers outperforms each retriever and larger models by +10.8% and +3.9% on average. |
Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities (2026.acl-long)
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Changdae Oh, Seongheon Park, To Eun Kim, Jiatong Li, Wendi Li, Samuel Yeh, Sean Du, Hamed Hassani, Paul Bogdan, Dawn Song, Sharon Li
| Challenge: | Uncertainty quantification (UQ) for large language models is a key building block for daily applications. |
| Approach: | They propose a general formulation of agent UQ that subsumes broad classes of existing UQ setups. |
| Outcome: | The proposed framework is based on the first general formulation of agent UQ that subsumes broad classes of existing setups. |