Introduces a new dataset and Average Severity Error metric for benchmarking LLMs on multi-label legal precedent treatment classification.
and Henderson, Peter and Ho, Daniel E
4 Pith papers cite this work, alongside 14 external citations. Polarity classification is still indexing.
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A new structured prompting method (SPEC) helps AI detect insufficient evidence in adjudication tasks and defer decisions appropriately, reaching 89% accuracy on a benchmark varying information completeness from Colorado unemployment insurance cases.
In 14 interviews, U.S. public defense professionals said AI is most helpful for evidence investigation and least helpful for courtroom advocacy and strategy.
Further pre-training ModernBERT on US court opinions improves results on legal datasets compared to the base model, with gains similar to early BERT domain adaptation work.
citing papers explorer
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Validate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification
Introduces a new dataset and Average Severity Error metric for benchmarking LLMs on multi-label legal precedent treatment classification.
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Learning When Not to Decide: A Framework for Overcoming Factual Presumptuousness in AI Adjudication
A new structured prompting method (SPEC) helps AI detect insufficient evidence in adjudication tasks and defer decisions appropriately, reaching 89% accuracy on a benchmark varying information completeness from Colorado unemployment insurance cases.
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How Can AI Augment Access to Justice? Public Defenders' Perspectives on AI Adoption
In 14 interviews, U.S. public defense professionals said AI is most helpful for evidence investigation and least helpful for courtroom advocacy and strategy.
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Legal Domain Adaptation of Modern BERT Models
Further pre-training ModernBERT on US court opinions improves results on legal datasets compared to the base model, with gains similar to early BERT domain adaptation work.