Papers by Yingcai Wu
StealthGraph: Exposing Domain-Specific Risks in LLMs through Knowledge-Graph-Guided Harmful Prompt Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Domain-specific datasets of harmful prompts are scarce and often rely on manual construction. Existing efforts to improve domain knowledge and reduce harmful prompt generation are lacking. |
| Approach: | They propose a framework that transforms domain knowledge into actionable constraints and increases the implicitness of generated harmful prompts. |
| Outcome: | The proposed framework yields high-quality datasets combining strong domain relevance with implicitness, enabling more realistic red-teaming and advancing LLM safety research. |
PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets with verifiable answers are limited in reliability, diversity, and scalability . a new approach to generate verifikatable data at scale is needed to improve models' performance . |
| Approach: | They propose a formal framework for synthesizing verifiable data at scale using a novel DSL-driven approach. |
| Outcome: | The proposed framework improves performance on a wide range of puzzles and logic benchmarks. |