KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration | ScienceToStartup | ScienceToStartup

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References (51)

[1]

Electrokinetic Effects on Flow and Ion Transport in Charge-Patterned Corrugated Nanochannels

2025Thomas Petersen, Pouya Golchin et al.

[2]

Position: Enough of Scaling LLMs! Lets Focus on Downscaling

2025Ayan Sengupta, Yash Goel et al.

[3]

Evaluating the Diversity and Quality of LLM Generated Content

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"Develop adaptive difficulty-calibrated MCQ datasets using a knowledge-graph framework to streamline LLM evaluation."

LLM Evaluation•Score: 6•View PDF ↗

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KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration

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References (51)

Founder's Pitch

"Develop adaptive difficulty-calibrated MCQ datasets using a knowledge-graph framework to streamline LLM evaluation."

Commercial Viability Breakdown

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KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration

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References (51)

Founder's Pitch

"Develop adaptive difficulty-calibrated MCQ datasets using a knowledge-graph framework to streamline LLM evaluation."

Commercial Viability Breakdown

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