We propose Self-Denoising Monte Carlo Annotation (SCAN), an efficient Process Reward Model (PRM) data synthesis and noise-tolerant learning framework.
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updated
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dyyyyyyyy/Qwen2.5-1.5B-GenRM-WithTemplate
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a model
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dyyyyyyyy/Qwen2.5-1.5B-GenRM-WithTemplate
Organizations
We introduce GNER, a Generative Named Entity Recognition framework, which demonstrates enhanced zero-shot capabilities across unseen entity domains.
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Rethinking Negative Instances for Generative Named Entity Recognition
Paper ā¢ 2402.16602 ā¢ Published ā¢ 3 -
dyyyyyyyy/GNER-LLaMA-7B
Text Generation ā¢ 7B ā¢ Updated ā¢ 18 ā¢ 4 -
dyyyyyyyy/GNER-T5-base
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 52 ā¢ 2 -
dyyyyyyyy/GNER-T5-large
Text Generation ā¢ 0.8B ā¢ Updated ā¢ 12 ā¢ 2
We introduce ScaleQuest, a scalable and novel data synthesis method. Project Page: https://scalequest.github.io/
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Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch
Paper ā¢ 2410.18693 ā¢ Published ā¢ 43 -
dyyyyyyyy/ScaleQuest-Math
Viewer ā¢ Updated ā¢ 1M ā¢ 92 ā¢ 23 -
dyyyyyyyy/ScaleQuest-Code
Viewer ā¢ Updated ā¢ 157k ā¢ 102 ā¢ 3 -
dyyyyyyyy/ScaleQuest-Math-Qwen2.5
Viewer ā¢ Updated ā¢ 622k ā¢ 50
We propose Self-Denoising Monte Carlo Annotation (SCAN), an efficient Process Reward Model (PRM) data synthesis and noise-tolerant learning framework.
We introduce ScaleQuest, a scalable and novel data synthesis method. Project Page: https://scalequest.github.io/
-
Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch
Paper ā¢ 2410.18693 ā¢ Published ā¢ 43 -
dyyyyyyyy/ScaleQuest-Math
Viewer ā¢ Updated ā¢ 1M ā¢ 92 ā¢ 23 -
dyyyyyyyy/ScaleQuest-Code
Viewer ā¢ Updated ā¢ 157k ā¢ 102 ā¢ 3 -
dyyyyyyyy/ScaleQuest-Math-Qwen2.5
Viewer ā¢ Updated ā¢ 622k ā¢ 50
We introduce GNER, a Generative Named Entity Recognition framework, which demonstrates enhanced zero-shot capabilities across unseen entity domains.
-
Rethinking Negative Instances for Generative Named Entity Recognition
Paper ā¢ 2402.16602 ā¢ Published ā¢ 3 -
dyyyyyyyy/GNER-LLaMA-7B
Text Generation ā¢ 7B ā¢ Updated ā¢ 18 ā¢ 4 -
dyyyyyyyy/GNER-T5-base
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 52 ā¢ 2 -
dyyyyyyyy/GNER-T5-large
Text Generation ā¢ 0.8B ā¢ Updated ā¢ 12 ā¢ 2