Papers by Taesik Gong
ExPerT: Personalizing LLM Responses to Users’ Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues (2026.acl-long)
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| Challenge: | Existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation. |
| Approach: | They propose a query-wise personalization framework that adapts LLM responses to query domain expertise by combining semantic and behavioral cues. |
| Outcome: | ExPerT reduces expertise inference error by 65.7% compared to the strongest baseline and improves response satisfaction by 17.52% on a 5-point Likert scale. |
By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Prompting (2024.emnlp-main)
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| Challenge: | Existing text-based prompts for large language models (LLMs) show performance degradation when handling long sensor data sequences. |
| Approach: | They propose a visual prompt that directs MLLMs to utilize visualized sensor data alongside descriptions of the target sensory task. |
| Outcome: | The proposed approach achieves 10% higher accuracy and reduces token costs by 15.8 times on nine sensory tasks involving four sensing modalities . |