GRAB is a benchmark dataset of 1.61M sentences from 8,247 10-K filings with taxonomy-anchored weak supervision labels for standardized evaluation of unsupervised topic models on financial risk disclosures.
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Pith papers citing it
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×-shaped variable-width transformers outperform parameter-matched uniform baselines on language modeling loss with 22% fewer FLOPs and 15% smaller KV cache.
Proposes an information-geometric method to compute potential diversity benchmarks and diversity gaps for biodiversity mapping using constrained variational principles on species compositions.
citing papers explorer
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GRAB: A Risk Taxonomy--Grounded Benchmark for Unsupervised Topic Discovery in Financial Disclosures
GRAB is a benchmark dataset of 1.61M sentences from 8,247 10-K filings with taxonomy-anchored weak supervision labels for standardized evaluation of unsupervised topic models on financial risk disclosures.
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Variable-Width Transformers
×-shaped variable-width transformers outperform parameter-matched uniform baselines on language modeling loss with 22% fewer FLOPs and 15% smaller KV cache.
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An information-geometric framework for mapping maximum potential biodiversity
Proposes an information-geometric method to compute potential diversity benchmarks and diversity gaps for biodiversity mapping using constrained variational principles on species compositions.