I'm thrilled to share our new paper, "FinForge: Semi-Synthetic Financial Benchmark Generation" in AI4Finance, AAAI'26! 
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I'm thrilled to share our new paper, "FinForge: Semi-Synthetic Financial Benchmark Generation" in AI4Finance, AAAI'26! 
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Key Contributions:
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Key Contributions:
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- FinForge Framework: A hybrid pipeline integrating manual/programmatic corpus construction with rigorous LM-based synthesis.
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- FinForge Framework: A hybrid pipeline integrating manual/programmatic corpus construction with rigorous LM-based synthesis.
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- FinForge-5k Dataset: A new snapshot benchmark comprising over 5,000 human-validated Q&A pairs across 11 financial subdomains, derived from a curated corpus of 100,000 verified documents (143M tokens).
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- FinForge-5k Dataset: A new snapshot benchmark comprising over 5,000 human-validated Q&A pairs across 11 financial subdomains, derived from a curated corpus of 100,000 verified documents (143M tokens).
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- Benchmarking Results: Evaluation of state-of-the-art open and closed-source models reveals significant variance in financial reasoning capabilities, with leading models achieving approximately 80% accuracy.
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- Benchmarking Results: Evaluation of state-of-the-art open and closed-source models reveals significant variance in financial reasoning capabilities, with leading models achieving approximately 80% accuracy.
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Huge thanks to my co-authors 
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Huge thanks to my co-authors 
@glennmatlin
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glennmatlin
 , Anant Gupta, Anirudh JM, Rayan Castilla, and Yi Mei Ng for this collaboration. 
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 , Anant Gupta, Anirudh JM, Rayan Castilla, and Yi Mei Ng for this collaboration. 
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You can read the paper: 
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You can read the paper: 
FinForge: Semi-Synthetic Financial Benchmark Generation (2601.06747)
https://huggingface.co/papers/2601.06747
2601.06747
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https://huggingface.co/papers/2601.06747
I'm thrilled to share our new paper, "FinForge: Semi-Synthetic Financial Benchmark Generation" in AI4Finance, AAAI'26! 

Key Contributions:
- FinForge Framework: A hybrid pipeline integrating manual/programmatic corpus construction with rigorous LM-based synthesis.
- FinForge-5k Dataset: A new snapshot benchmark comprising over 5,000 human-validated Q&A pairs across 11 financial subdomains, derived from a curated corpus of 100,000 verified documents (143M tokens).
- Benchmarking Results: Evaluation of state-of-the-art open and closed-source models reveals significant variance in financial reasoning capabilities, with leading models achieving approximately 80% accuracy.

Huge thanks to my co-authors @glennmatlin , Anant Gupta, Anirudh JM, Rayan Castilla, and Yi Mei Ng for this collaboration. 

You can read the paper: https://huggingface.co/papers/2601.06747
👀
John6666 glennmatlin