Pangu-ACE improves educational response quality on EduBench from 0.457 to 0.538 and format validity from 0.707 to 0.866 by routing 19.7% of samples to a 1B model while escalating the rest to 7B.
System 2 atten- tion (is something you might need too)
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A systematic survey categorizes prompt engineering methods for LLMs and VLMs by application area, summarizing methodologies, applications, models, datasets, strengths, and limitations for each technique along with a taxonomy and summary table.
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Pangu-ACE: Adaptive Cascaded Experts for Educational Response Generation on EduBench
Pangu-ACE improves educational response quality on EduBench from 0.457 to 0.538 and format validity from 0.707 to 0.866 by routing 19.7% of samples to a 1B model while escalating the rest to 7B.
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A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
A systematic survey categorizes prompt engineering methods for LLMs and VLMs by application area, summarizing methodologies, applications, models, datasets, strengths, and limitations for each technique along with a taxonomy and summary table.