Papers by Yijing Zhang
Pub-LawBench: Public-Oriented Benchmarking for LegalAI (2026.acl-long)
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| Challenge: | Existing evaluation frameworks focus on legal professionals, not legal professionals. |
| Approach: | They propose a public-oriented LegalAI benchmark grounded in legal functionalism and genre analysis to address this gap. |
| Outcome: | The proposed model evaluates 17 large language models on Pub-LawBench using simple prompts and Chain-of-Thought under a vanilla inference setting. |
Personalize Your LLM: Fake it then Align it (2025.findings-naacl)
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| Challenge: | Existing personalization methods require fine-tuning of large language models for each user, rendering them prohibitively expensive for widespread adoption. |
| Approach: | They propose a retrieval-based personalization approach that uses self-generated personal preference data and representation editing to enable quick and cost-effective personalization. |
| Outcome: | The proposed approach outperforms two personalization baselines by 40% on various tasks. |