Papers by Seongho Joo
Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encoding (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities but their misuse for harmful purposes remains a concern. |
| Approach: | They propose a jailbreaking technique that exploits weaknesses in LLMs' architecture . they propose abductive framing and symbolic encoding to bypass safeguards . |
| Outcome: | The proposed technique achieves over 95% attack success rate on GPT-series models and 70% across all targets. |
Drift: Decoding-time Personalized Alignments with Implicit User Preferences (2025.findings-emnlp)
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| Challenge: | Drift personalizes large language models at decoding time with implicit user preferences . Unlike traditional Reinforcement Learning from Human Feedback, Drift operates in a training-free manner . |
| Approach: | They propose a framework that personalizes large language models at decoding time with implicit user preferences. |
| Outcome: | The proposed framework personalizes large language models at decoding time with implicit user preferences. |
Public Data Assisted Differentially Private In-Context Learning (2025.findings-emnlp)
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| Challenge: | In-context learning has shown remarkable performance across tasks without fine-tuning . however, recent studies have highlighted the risk of private data leakage through the prompt in ICL . |
| Approach: | They propose a private in-context learning algorithm that effectively balances privacy protection and model utility. |
| Outcome: | The proposed algorithm is robust against membership inference attacks and is robust to membership infertility attacks. |
DPP-TTS: Diversifying prosodic features of speech via determinantal point processes (2023.emnlp-main)
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| Challenge: | Recent advances in deep generative models have succeeded in synthesizing human-like speech. |
| Approach: | They propose a text-to-speech model with a prosody diversifying module that considers perceptual diversity in each sample and among multiple samples. |
| Outcome: | The proposed model generates speech samples with more diversified prosody than baselines in the side-by-side comparison test considering the naturalness of speech at the same time. |