Self- and GPT-augmented performance peaks and degrades after 55x–89x augmentation, while Llama-augmented performance keeps rising toward 100x¶

Claim · All claims
Evidence · 1 study · causal r1 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Self- and GPT-augmented models peaked and degraded after 55x to 89x augmentation, but Llama-augmented models continued improving up to 100x, partially supporting the degradation hypothesis. → Keith Cochran 2025

Evidence¶

Keith Cochran 2025¶

Keith Cochran, Clayton Cohn, Jean-François Rouet, Peter Hastings. (2025). Comparing Text Augmentation by GPT-3.5 and Llama3 for Evaluating Student Responses. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-025-00473-x

q2 · i? · causal · r1

Trend analysis of performance-versus-augmentation curves across the six augmentation methods and four classifiers, reported in the Discussion of RQ4. Llama models "continued to rise as the augmentation neared 100x".

"In self and GPT-augmented models tested, performance peaked and degraded after 55x to 89x augmentation. For the Llama-augmented models, performance continued to rise as the augmentation neared 100x, suggesting more augmentation could be applied above 100x to achieve even higher performance."

Discussion¶

Related Claims¶

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