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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ALLaVA creates 1.3M GPT4V-synthesized samples enabling 4B VLMs to achieve competitive results on 17 benchmarks and match 7B/13B models on some tasks.
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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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ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
ALLaVA creates 1.3M GPT4V-synthesized samples enabling 4B VLMs to achieve competitive results on 17 benchmarks and match 7B/13B models on some tasks.