Papers by Sangkwon Park

3 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 .
Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models (2025.findings-acl)

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Challenge: Recent advances in multi-turn voice interaction models have improved user-model communication, but whether open-source models share this ability remains unexplored.
Approach: They propose to use ContextDialog to evaluate open-source interaction models' ability to recall past utterances to identify key limitations.
Outcome: The proposed model retains and recalls past utterances better than closed-source models, but still struggles with questions about past . findings highlight key limitations in open-source model and suggest ways to improve memory retention and retrieval robustness.
Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning (2026.acl-long)

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Challenge: Existing paradigms of Retrieval-Augmented Generation (RAG) are suboptimal due to exposure bias, a mismatch between pre-training data distribution and retrieved information.
Approach: They propose to bridge retrieval results and the LLM’s reasoning ability through Verbal Annotations, analytic narratives that explicitly articulate the logical connection between a search query and retrieved contexts.
Outcome: The proposed framework achieves state-of-the-art performance on complex Question Answering benchmarks validating the effectiveness of the proposed framework.

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