#mathinstruct search results
A natural question to ask: Why 🦣#MAmmoTH is so powerful😱? We investigate how the two major characteristics of #MathInstruct influence the performance of 🦣. Main takeaway: Diverse data sources and hybrid CoT & PoT training lead to substantial gains, making 🦣 math generalists.
Introducing 🦣MAmmoTH: The BEST open-source #LLMs for math NOW! 🦣Outperforms SOTA on 9 math reasoning datasets, with accuracy gains of 13-29% across all scales. 🦣 is tuned on our 260K #MathInstruct dataset, including hybrid CoT & PoT rationales. #NLProc tiger-ai-lab.github.io/MAmmoTH/
🚀Our instruction-tuning dataset #MathInstruct is compiled from 13 math datasets, 6 of which have rationales newly curated by us. What set #MathInstruct apart? 1️⃣Broad coverage of different math fields and complexity levels 2️⃣Hybrid CoT & PoT rationales
Enter MathInstruct: A novel hybrid dataset combining Chain-of-Thought (CoT) and code-based techniques. It offers comprehensive coverage of various mathematical areas. 📘 #MathInstruct #CoT #CodeBased
#MAmmoTH LLMs are changing math problem-solving, outperforming existing models by 13-29%! Trained on #MathInstruct dataset. More info: arxiv.org/abs/2309.05653…
Enter MathInstruct: A novel hybrid dataset combining Chain-of-Thought (CoT) and code-based techniques. It offers comprehensive coverage of various mathematical areas. 📘 #MathInstruct #CoT #CodeBased
#MAmmoTH LLMs are changing math problem-solving, outperforming existing models by 13-29%! Trained on #MathInstruct dataset. More info: arxiv.org/abs/2309.05653…
A natural question to ask: Why 🦣#MAmmoTH is so powerful😱? We investigate how the two major characteristics of #MathInstruct influence the performance of 🦣. Main takeaway: Diverse data sources and hybrid CoT & PoT training lead to substantial gains, making 🦣 math generalists.
🚀Our instruction-tuning dataset #MathInstruct is compiled from 13 math datasets, 6 of which have rationales newly curated by us. What set #MathInstruct apart? 1️⃣Broad coverage of different math fields and complexity levels 2️⃣Hybrid CoT & PoT rationales
Introducing 🦣MAmmoTH: The BEST open-source #LLMs for math NOW! 🦣Outperforms SOTA on 9 math reasoning datasets, with accuracy gains of 13-29% across all scales. 🦣 is tuned on our 260K #MathInstruct dataset, including hybrid CoT & PoT rationales. #NLProc tiger-ai-lab.github.io/MAmmoTH/
A natural question to ask: Why 🦣#MAmmoTH is so powerful😱? We investigate how the two major characteristics of #MathInstruct influence the performance of 🦣. Main takeaway: Diverse data sources and hybrid CoT & PoT training lead to substantial gains, making 🦣 math generalists.
🚀Our instruction-tuning dataset #MathInstruct is compiled from 13 math datasets, 6 of which have rationales newly curated by us. What set #MathInstruct apart? 1️⃣Broad coverage of different math fields and complexity levels 2️⃣Hybrid CoT & PoT rationales
Introducing 🦣MAmmoTH: The BEST open-source #LLMs for math NOW! 🦣Outperforms SOTA on 9 math reasoning datasets, with accuracy gains of 13-29% across all scales. 🦣 is tuned on our 260K #MathInstruct dataset, including hybrid CoT & PoT rationales. #NLProc tiger-ai-lab.github.io/MAmmoTH/
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