Papers by Ik-hwan Kim

2 papers
Exploring the Potential of LLMs as Personalized Assistants: Dataset, Evaluation, and Analysis (2025.acl-long)

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Challenge: Personalized AI assistants are a challenging application that intertwines multiple problems in LLM research.
Approach: They propose a Llama-3.2-based automated evaluation model that matches human preferences to a conversational dataset.
Outcome: HiCUPID provides a conversational dataset tailored for personalization . the evaluation model closely mirrors human preferences, the researchers show .
Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context (2025.findings-naacl)

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Challenge: Multi-hop reasoning requires multi-step reasoning based on supporting documents within a given context.
Approach: They propose a method that prompts the model by repeatedly presenting the context.
Outcome: The proposed method improves the F1 score by 30%p on multi-hop QA tasks and increases accuracy by 70%p on a synthetic task.

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