LePREC combines LLM-generated analytical QA factors with sparse linear classification to achieve 30-40% better precision than pure LLMs in identifying relevant legal issues from court cases.
The Appellants claimed late delivery of the property, seeking liquidated ascertained damages (LAD) based on delays exceeding the stipulated timeframes for completion
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
LePREC: Reasoning as Classification over Structured Factors for Assessing Relevance of Legal Issues
LePREC combines LLM-generated analytical QA factors with sparse linear classification to achieve 30-40% better precision than pure LLMs in identifying relevant legal issues from court cases.