Papers by Lucia Zheng
LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain (2025.findings-naacl)
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Joel Niklaus, Lucia Zheng, Arya D. McCarthy, Christopher Hahn, Brian M Rosen, Peter Henderson, Daniel E. Ho, Garrett Honke, Percy Liang, Christopher D Manning
| Challenge: | In general, instruction tuning is important for direct user interaction, but the legal domain is underrepresented in typical instruction datasets. |
| Approach: | They aggregate 58 annotated legal datasets and write instructions for each to create LawInstruct. |
| Outcome: | The proposed model improves on LegalBench across all model sizes, but no drop in MMLU. |
NLP Systems That Can’t Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helps (2024.naacl-long)
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| Challenge: | Existing language models fail to distinguish use from mention, leading to misinformation and hate speech detection, resulting in censorship of counterspeech. |
| Approach: | They propose prompting mitigations that teach the use-mention distinction and show they reduce these errors. |
| Outcome: | The proposed model reduces misinformation and hate speech detection errors by reducing misinformation, and reducing hate speech. |