Audit finds 36-39% incorrect FOL labels in FOLIO and MALLS; corrections raise LLM accuracy 9-22 points and an LLM-guided review framework achieves 90% dataset quality after checking fewer than 24% of examples.
arXiv preprint arXiv:2505.21281 (2025)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A 1D CNN with FastText embeddings classifies legal texts at 97.26% accuracy using 5.1 million parameters and runs over 13 times faster than BERT.
citing papers explorer
-
Fixing FOLIO and MALLS: Verified Annotations and an LLM-assisted Framework to Focus Human Relabeling
Audit finds 36-39% incorrect FOL labels in FOLIO and MALLS; corrections raise LLM accuracy 9-22 points and an LLM-guided review framework achieves 90% dataset quality after checking fewer than 24% of examples.
-
Towards Intelligent Legal Document Analysis: CNN-Driven Classification of Case Law Texts
A 1D CNN with FastText embeddings classifies legal texts at 97.26% accuracy using 5.1 million parameters and runs over 13 times faster than BERT.