Papers by Mengxiang Li
Dual Prompt Tuning based Contrastive Learning for Hierarchical Text Classification (2024.findings-acl)
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| Challenge: | Existing methods focus on hierarchy-aware text feature by exploiting explicit parent-child relationships, resulting in label confusion within each layer. |
| Approach: | They propose a dual-prompt tuning method which emphasizes discrimination among peer labels by performing contrastive learning on each hierarchical layer. |
| Outcome: | The proposed method outperforms existing methods on benchmark datasets and is available on github. |
Beyond Static Personas: Situational Personality Steering for Large Language Models (2026.findings-acl)
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| Challenge: | Existing personalization methods rely on static personality modeling to achieve optimal performance. |
| Approach: | They propose a training-free framework for advanced situational personality steering that incorporates situation-dependent behavior patterns within LLM personalities through analysis of persona neurons. |
| Outcome: | The proposed framework surpasses baselines on PersonalityBench and SPBench, demonstrating generalization and robustness to complex, unseen situations and different models architecture. |