Rich Insights from Cheap Signals: Efficient Evaluations via Tensor Factorization | ScienceToStartup | ScienceToStartup

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References (51)

[1]

Benchmarking Large Language Models for Personalized Guidance in AI-Enhanced Learning

2025Bo Yuan, Jiazi Hu

[2]

Bridging Human and LLM Judgments: Understanding and Narrowing the Gap

2025Felipe Maia Polo, Xinhe Wang et al.

[3]

Scaling Up Active Testing to Large Language Models

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"Develop a statistical model that efficiently evaluates generative models using tensor factorization to merge autorater data with limited human labels."

AI Evaluation Techniques•Score: 2•View PDF ↗

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2.5

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1/4 signals

2.5

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Rich Insights from Cheap Signals: Efficient Evaluations via Tensor Factorization

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References (51)

Founder's Pitch

"Develop a statistical model that efficiently evaluates generative models using tensor factorization to merge autorater data with limited human labels."

Commercial Viability Breakdown

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Rich Insights from Cheap Signals: Efficient Evaluations via Tensor Factorization

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References (51)

Founder's Pitch

"Develop a statistical model that efficiently evaluates generative models using tensor factorization to merge autorater data with limited human labels."

Commercial Viability Breakdown

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