Papers by Changbong Kim

2 papers
SLM as Guardian: Pioneering AI Safety with Small Language Model (2024.emnlp-industry)

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Challenge: Prior safety research on large language models focused on aligning them to safety requirements, but internalizing such safeguard features into larger models brought challenges of higher training cost and unintended degradation of helpfulness.
Approach: They propose a multi-task learning mechanism that integrates harmful query detection and safeguard response into a single model.
Outcome: The proposed approach outperforms the publicly available LLMs in harmful query detection and safeguard response generation.
Taxonomy and Analysis of Sensitive User Queries in Generative AI Search System (2025.findings-naacl)

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Challenge: generative LLMs have been used by industries for various purposes, but limited resources and limited experience hinder their deployment and maintenance.
Approach: They propose a taxonomy for sensitive search queries and outline approaches to generating generative LLMs.
Outcome: The proposed model can be used to analyze sensitive queries from real users.

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