Papers by Tsung-Yi Ho
GRE Score: Generative Risk Evaluation for Large Language Models (2026.findings-acl)
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| Challenge: | Large language models have revolutionized generative tasks, but concerns about their trustworthiness and vulnerability to adversarial attacks persist. |
| Approach: | They propose an attack-independent evaluation of LLM robustness using conditional generation for synthetic text creation and a method to quantify the model's resilience. |
| Outcome: | The proposed method achieves a consistent ranking of LLM robustness when compared to the attack-based model ranking on TrustLLM (CITATION). |
Hey, That’s My Data! Token-Only Dataset Inference in Large Language Models (2026.findings-acl)
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| Challenge: | Existing dataset inference methods require logit access, but many modern LLMs restrict such access. |
| Approach: | They propose a token-only dataset inference framework that allows models to overwrite prior knowledge when trained on new data. |
| Outcome: | The proposed framework overwrites prior knowledge when trained on new data. |
Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets (2026.acl-long)
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| Challenge: | Existing mitigation strategies focus on reactively addressing jailbreak incidents after safety guardrails have been compromised. |
| Approach: | They investigate the degradation of safety guardrails through the lens of representation similarity between upstream alignment datasets and downstream fine-tuning tasks. |
| Outcome: | The proposed model reduces harmfulness score by 10.33% when compared to baseline models. |
Defensive Prompt Patch: A Robust and Generalizable Defense of Large Language Models against Jailbreak Attacks (2025.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have showcased their ability to understand and generate text akin to human interaction. |
| Approach: | They propose a prompt-based defense mechanism specifically designed to protect LLMs against jailbreak attacks by introducing jailbreak prompts into malicious queries. |
| Outcome: | Empirical results show that the proposed defense outperforms existing defense strategies in balancing safety and utility while maintaining high utility. |