Papers by Yingxu Li
Human-Inspired Obfuscation for Model Unlearning: Local and Global Strategies with Hyperbolic Representations (2025.findings-emnlp)
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| Challenge: | Existing methods for unlearning large language models struggle to balance effective forgetting with maintaining model utility. |
| Approach: | They propose a human-inspired unlearning framework that simulates forgetting on fuzzy data and represents them in hyperbolic and Euclidean spaces. |
| Outcome: | The proposed framework is able to forget sensitive content while maintaining the model’s language understanding, fluency, and benchmark performance. |
GLA: Grounding Large Language Models in Molecular Hierarchy for Chemical Understanding (2026.findings-acl)
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| Challenge: | Existing molecule-language models obscure the hierarchical organization of chemical semantics . Existing models rely on linear or uniform encodings, causing structural distortion . |
| Approach: | They propose a framework that integrates intrinsic molecular topology into large language models. |
| Outcome: | The proposed framework improves on cross-modal retrieval, captioning, and property prediction benchmarks. |
LOTUS: Evolving Multimodal Unlearning via Hyperbolic Entailment and Lorentz Transport (2026.acl-long)
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| Challenge: | Existing unlearning methods suffer from a geometric mismatch, causing catastrophic forgetting or unsafe substitution. |
| Approach: | They propose a framework for surgical semantic pruning within the Lorentz manifold. |
| Outcome: | Experiments on MLLMU-Bench show that LOTUS significantly outperforms baselines while maintaining general utility. |