Papers by Peter Henderson
With Little Power Comes Great Responsibility (2020.emnlp-main)
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| Challenge: | Underpowered experiments make it more difficult to discern the difference between statistical noise and meaningful model improvements and increase the chances of exaggerated findings. |
| Approach: | They characterize typical statistical power for a variety of settings and characterize it by a set of existing NLP papers and datasets. |
| Outcome: | The authors characterize typical power for a variety of settings and find it common in the literature. |
Text Characterization Toolkit (TCT) (2022.aacl-demo)
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Daniel Simig, Tianlu Wang, Verna Dankers, Peter Henderson, Khuyagbaatar Batsuren, Dieuwke Hupkes, Mona Diab
| Challenge: | Text Characterization Toolkit (TCT) is a tool that researchers can use to study characteristics of large datasets. |
| Approach: | They propose a text characterization toolkit that researchers can use to study characteristics of large datasets. |
| Outcome: | The proposed tool can be used to study characteristics of large datasets and to understand the influence of attributes on models’ behaviour. |
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. |