KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration | Paper Citation Record

Observation d6e5a9f9-97e1-454a-82d9-5b015875e16f · outbound

This paper cites Scaling Laws for Neural Language Models.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Scaling Laws for Neural Language Models

Reference 1

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Observation 34720a21-6de5-4f1d-b168-1677e75f9ad7 · outbound

This paper cites A holistic assessment of the carbon footprint of noor, a very large Arabic language model.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration A holistic assessment of the carbon footprint of noor, a very large Arabic language model

Reference 2

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Observation 91c44da1-2816-41a9-902b-ed526915e63f · outbound

This paper cites Position: Enough of Scaling LLMs! Lets Focus on Downscaling.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Position: Enough of Scaling LLMs! Lets Focus on Downscaling

Reference 3

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Observation b2a8c2af-16f4-4cbd-a65c-602812e27642 · outbound

This paper cites Compendium of llm evaluation methods.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Compendium of llm evaluation methods

Reference 4

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Observation e6ac5c62-146f-47a4-b2af-54fde30c05e4 · outbound

This paper cites Ragas: Supercharge your llm application evaluations.https://github.c om/explodinggradients/ragas, 2024.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Ragas: Supercharge your llm application evaluations.https://github.c om/explodinggradients/ragas, 2024

Reference 5

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Observation 5ef0b6e0-385d-4d0f-8f91-4f838f169b7f · outbound

This paper cites Leaf: Multiple-choicequestion generation.

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

Reference 6

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Observation f7f4ddb0-f909-4691-9525-806de2401daf · outbound

This paper cites Multiple-Choice Question Generation: Towards an Automated Assessment Framework.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Multiple-Choice Question Generation: Towards an Automated Assessment Framework

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:00.403468Z

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Observation a6280d3c-fe84-4d66-9c4b-8201a34c09e2 · outbound

This paper cites Multiple-choice question generation using large language models: Methodology and educator insights.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Multiple-choice question generation using large language models: Methodology and educator insights

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:00.513506Z

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Observation 2b683a59-4180-4190-bd27-435e0180144e · outbound

This paper cites Measuring Massive Multitask Language Understanding.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Measuring Massive Multitask Language Understanding

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:00.760652Z

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Observation 646dd6a4-961c-41cc-b0a9-b361aec6df73 · outbound

This paper cites itext2kg: Incremental knowledge graphs construction using large language models.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration itext2kg: Incremental knowledge graphs construction using large language models

Reference 11

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Observation 5744131a-7864-44f9-8458-21a0cc0be075 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:01.010063Z

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Observation d7ed7889-3bbe-4a59-8dfe-b59a7d652b4e · outbound

This paper cites InProceedings of the 37th International Conference on Machine Learning (ICML 2020), pages 3929–3938, 2020.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration InProceedings of the 37th International Conference on Machine Learning (ICML 2020), pages 3929–3938, 2020

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:01.129427Z

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Observation 2dd5f49d-4b45-4cc5-9572-d3c5ced2080a · outbound

This paper cites InProceedings of EMNLP 2021, 2021.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration InProceedings of EMNLP 2021, 2021

Reference 14

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Observation 08e77548-edf8-4f06-b9b1-69ba62712c71 · outbound

This paper cites A comprehensive survey on automatic knowledge graph construction.ACM Computing Surveys, 56(4):1–62, 2023.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration A comprehensive survey on automatic knowledge graph construction.ACM Computing Surveys, 56(4):1–62, 2023

Reference 16

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Observation 79a44c95-a557-44c6-80dd-66cd6c430458 · outbound

This paper cites Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann

Reference 17

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Observation 54dbdb07-7ca5-4b92-9f11-bc63393429b6 · outbound

This paper cites an unresolved cited work.

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

Reference 18

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Observation 42ce36c5-ec19-4951-b5ef-5162bb6ce136 · outbound

This paper cites Llms for knowledge graph construction and reasoning: Recent capabilities and future opportunities.World Wide Web, 27(5):58, 2024.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Llms for knowledge graph construction and reasoning: Recent capabilities and future opportunities.World Wide Web, 27(5):58, 2024

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:01.575609Z

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Observation a7d7be84-47be-4a4e-8379-f13b0b1f44ab · outbound

This paper cites GPT-4 Technical Report.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration GPT-4 Technical Report

Reference 20

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Observation d78f74fb-8e25-4e70-9ee7-94fd2f0c4485 · outbound

This paper cites Wiki-based prompts for enhancing relation extraction using language models.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Wiki-based prompts for enhancing relation extraction using language models

Reference 21

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Observation 0db4e54e-ecfc-4926-b382-b3c950b81d63 · outbound

This paper cites Wikidata: A free collaborative knowledgebase.Com- munications of the ACM, 57(10):78–85, 2014.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Wikidata: A free collaborative knowledgebase.Com- munications of the ACM, 57(10):78–85, 2014

Reference 22

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Observation b4a15c08-3b28-4ab4-aed4-eb7c0d6f71a2 · outbound

This paper cites Khapra, and Sachindra Joshi.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Khapra, and Sachindra Joshi

Reference 23

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Observation 4c3e1a56-7909-46de-bd18-0afe96fe7a97 · outbound

This paper cites Toward subgraph-guided knowledge graph question generation with graph neural networks.IEEE Transactions on Neural Networks and Learning Systems, 2023.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Toward subgraph-guided knowledge graph question generation with graph neural networks.IEEE Transactions on Neural Networks and Learning Systems, 2023

Reference 24

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Observation e3214449-b528-41ac-a222-c463de0566be · outbound

This paper cites Multi-hopquestiongeneration with knowledge graph-enhanced language model.Applied Sciences, 13(9):5765, 2023.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Multi-hopquestiongeneration with knowledge graph-enhanced language model.Applied Sciences, 13(9):5765, 2023

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:01.990062Z

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Observation ad513707-1181-4861-ae3b-b4f1991a83ea · outbound

This paper cites Difficulty-controllable multi-hop question generation from knowledge graphs.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Difficulty-controllable multi-hop question generation from knowledge graphs

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:02.105871Z

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Observation e54c0675-a1be-403b-a9cf-d504dff60ac6 · outbound

This paper cites Guidingthegrowth: Difficulty-controllablequestiongenerationthroughstep-by-steprewriting.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Guidingthegrowth: Difficulty-controllablequestiongenerationthroughstep-by-steprewriting

Reference 27

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Observation 15ae3ed1-0c65-4d64-bf7b-07078c5fd8d3 · outbound

This paper cites Liquid: aframeworkforlistquestionanswering dataset generation.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Liquid: aframeworkforlistquestionanswering dataset generation

Reference 28

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Observation 8d91008f-8942-4600-93ee-33b15c737415 · outbound

This paper cites An automatic question usabilityevaluationtoolkit.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration An automatic question usabilityevaluationtoolkit

Reference 29

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Observation c435d6a6-359c-4bfb-9c49-73e790bc4f1a · outbound

This paper cites Evaluating the diversity and quality of llm generated content.arXiv preprint arXiv:2504.12522, 2025.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Evaluating the diversity and quality of llm generated content.arXiv preprint arXiv:2504.12522, 2025

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:02.574005Z

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Observation 97e6083e-cf9a-4c01-9e59-291d7a03cf27 · outbound

This paper cites Towards Trustable Language Models: Investigating Information Quality of Large Language Models.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Towards Trustable Language Models: Investigating Information Quality of Large Language Models

Reference 31

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Observation 443a8004-a421-4056-a19f-d12e91f0cda6 · outbound

This paper cites The curious case of hallucinatory (un)answerability: Finding truths in the hidden states of over-confident large language models.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration The curious case of hallucinatory (un)answerability: Finding truths in the hidden states of over-confident large language models

Reference 32

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Observation 4b495a53-4787-4a6e-b157-3dc4e97e47d0 · outbound

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

Reference 33

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Observation 5eb85dea-d2da-40ed-b48c-30e82e7d90f9 · outbound

This paper cites Automatic multiple-choice question generation and evaluation systems based on LLM: A study case with university resolutions.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Automatic multiple-choice question generation and evaluation systems based on LLM: A study case with university resolutions

Reference 34

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Observation c4c982e6-1c11-4ce8-b370-86f0a591ab58 · outbound

This paper cites Adversarial NLI: A new benchmark for natural language understanding.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Adversarial NLI: A new benchmark for natural language understanding

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:03.011141Z

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Observation 0f2cf312-f260-467b-858c-64794e730e99 · outbound

This paper cites Bowman, Gabor Angeli, Christopher Potts, and Christopher D.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Bowman, Gabor Angeli, Christopher Potts, and Christopher D

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:03.121870Z

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Observation 05e80369-9717-4a82-8a12-3316352d3e58 · outbound

This paper cites an unresolved cited work.

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

Reference 37

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Observation af3a5eaa-a628-4333-9dd6-e8eb036f9f03 · outbound

This paper cites Unsuper- vised dense information retrieval with contrastive learning.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Unsuper- vised dense information retrieval with contrastive learning

Reference 38

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Observation 0abd2c5f-eaaf-411f-8f18-169bdc0b0343 · outbound

This paper cites The Probabilistic Relevance Framework: BM25 and beyond.Foundations and Trends in Information Retrieval, 2009.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration The Probabilistic Relevance Framework: BM25 and beyond.Foundations and Trends in Information Retrieval, 2009

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:03.385947Z

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Observation d08de2d4-9fee-412e-b059-1ac286ce9370 · outbound

This paper cites Making monolingual sentence embeddings multilingual using knowledge distillation.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Making monolingual sentence embeddings multilingual using knowledge distillation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:03.520660Z

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Observation 7cc22c69-aea1-4f43-8a1d-2285904d1abf · outbound

This paper cites Survey of hallucination in natural language generation.ACM Computing Surveys, 2023.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Survey of hallucination in natural language generation.ACM Computing Surveys, 2023

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:03.660636Z

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Observation ddb35199-6217-4bf0-8fed-cbe3e8fb3e90 · outbound

This paper cites Wikidata: A free collaborative knowledgebase.Com- munications of the ACM, 57(10):78–85, 2014.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Wikidata: A free collaborative knowledgebase.Com- munications of the ACM, 57(10):78–85, 2014

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:03.734634Z

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Observation aeea4137-95c1-48ff-abf6-d3f6ed5bbee6 · outbound

This paper cites Large language models as distractor generators for multiple-choice qa.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Large language models as distractor generators for multiple-choice qa

Reference 43

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Observation aae7dcea-e652-479e-81d7-394eaaff81d8 · outbound

This paper cites Haladyna, Steven M.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Haladyna, Steven M

Reference 44

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Observation 2be10554-2201-47f1-a9ea-538a732551d0 · outbound

This paper cites Analyzingquestioncharacteristicsinfluencingchatgpt’sperformancein3000usmle®-style questions.Medical Science Educator, pages 1–11, 2024.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Analyzingquestioncharacteristicsinfluencingchatgpt’sperformancein3000usmle®-style questions.Medical Science Educator, pages 1–11, 2024

Reference 45

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Observation 28dad95c-580c-42f8-b430-4b9794e52db8 · outbound

This paper cites Can LLMs Solve longer Math Word Problems Better?.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Can LLMs Solve longer Math Word Problems Better?

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:04.103263Z

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Observation eff8e1e3-656e-43ba-ab1e-0bcc78afbc34 · outbound

This paper cites Do Large Language Models have Shared Weaknesses in Medical Question Answering?.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Do Large Language Models have Shared Weaknesses in Medical Question Answering?

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:04.212661Z

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Observation 6c14175f-789e-42b3-a8c1-f14c5f5a22bf · outbound

This paper cites Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:04.322578Z

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Observation 7d8145e8-29c4-4a24-8287-941795caec47 · outbound

This paper cites Languagetool: Open-source grammar, style, and spell checker.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Languagetool: Open-source grammar, style, and spell checker

Reference 49

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Observation 9a32762d-1c1b-4aae-ac66-4df3166890de · outbound

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KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration language-tool-python: Python wrapper for languagetool

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:04.526352Z

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Observation 1e59b059-c167-40ef-8f8a-b96fe3e18a1a · outbound

This paper cites Langcheck: Simple, pythonic building blocks to evaluate llm applications.https: //github.com/citadel-ai/langcheck, 2023.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Langcheck: Simple, pythonic building blocks to evaluate llm applications.https: //github.com/citadel-ai/langcheck, 2023

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:04.589907Z

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Observation a3b8b527-6415-4a77-a257-eb91bbc6821d · outbound

This paper cites an unresolved cited work.

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

Reference 52

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Observation 524f999f-0a4f-4702-97c9-99bfb40bbc2e · outbound

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

Reference 53

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Observation f8206dd9-4d5a-4594-b66a-7e743982a87b · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 54

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Observation d7faddfe-53d6-4401-9d4d-46e78ac4c91c · outbound

This paper cites CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean

Reference 55

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Observation c0e6122f-82de-4eea-a015-247455104725 · outbound

This paper cites Llama: Open and efficient foundation language models.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Llama: Open and efficient foundation language models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:05.049645Z

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Observation 900ea719-fbd0-4aec-b332-0e1b4a605f21 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:05.172485Z

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Observation 58353252-9c08-4998-8eb3-e9db0e5d9f27 · outbound

This paper cites Commonsenseqa: A question answering challenge targeting commonsense knowledge.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Commonsenseqa: A question answering challenge targeting commonsense knowledge

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:05.275065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5915ff81-5fe2-4365-b683-bbef25d1a5b2 · outbound

This paper cites RACE: Large-scale ReAding Comprehension Dataset From Examinations.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration RACE: Large-scale ReAding Comprehension Dataset From Examinations

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:05.469582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d5397d4-606d-4ed1-b0cb-ae03251976d4 · outbound

This paper cites Medmcqa: A large- scale multi-subject multi-choice dataset for medical domain question answering.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Medmcqa: A large- scale multi-subject multi-choice dataset for medical domain question answering

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:05.607884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3b63bc3-d0bd-4520-aebd-14633f218ae8 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:05.674272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9abee72f-ed8b-4cc7-bed1-4b05ec0a9710 · outbound

This paper cites Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:05.759485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5626c601-7a46-4b1d-ac44-734177dd876a · outbound

This paper cites GPT-4o: System card and model overview.https://openai.com/index/gpt-4o-sys tem-card/, 2024.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration GPT-4o: System card and model overview.https://openai.com/index/gpt-4o-sys tem-card/, 2024

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:05.848584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb77f2f8-bfba-43ed-a2d8-cbf2fb76d3b9 · outbound

This paper cites Mistral large.

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

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:05.986401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fde9491-ff77-4ad7-b44f-df67db736531 · outbound

This paper cites Llama 3 model card and evaluations.https://ai.meta.com/llama/, 2024.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Llama 3 model card and evaluations.https://ai.meta.com/llama/, 2024

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:06.139091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b788e437-4539-4274-a2f1-ee874c4a4f57 · outbound

This paper cites Claude 3 model family: Model card and system overview.https://www.anthropic.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Claude 3 model family: Model card and system overview.https://www.anthropic

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:06.289567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52a13dd3-2d13-4712-a64d-9606bef1901a · outbound

This paper cites Qwen Technical Report.

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

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:06.479903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e53cd7e-d2ef-422b-9988-0cd2535b01ca · outbound

This paper cites Gemma: Openmodelsbuiltfromtheresearchbehind gemini.https://ai.google.dev/gemma, 2024.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Gemma: Openmodelsbuiltfromtheresearchbehind gemini.https://ai.google.dev/gemma, 2024

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:06.629734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01d4d6d3-2e04-4c0d-8a2a-e812ccf703a4 · outbound

This paper cites Electrokinetic Effects on Flow and Ion Transport in Charge-Patterned Corrugated Nanochannels.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Electrokinetic Effects on Flow and Ion Transport in Charge-Patterned Corrugated Nanochannels

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:06.768778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a08f053b-f1f3-4db0-b126-3c5230327948 · outbound

This paper cites Computing the saturation throughput for heterogeneous p-csma in a general wireless network.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Computing the saturation throughput for heterogeneous p-csma in a general wireless network

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:06.886985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c5de842-0374-4d66-96d4-82193295e813 · outbound

This paper cites langchain: Build context-aware reasoning applications.https://github.com/l angchain-ai/langchain, 2025.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration langchain: Build context-aware reasoning applications.https://github.com/l angchain-ai/langchain, 2025

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:07.062461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17ce8f65-ee65-4fda-b881-e2b19ef48085 · outbound

This paper cites spaCy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing.https://spacy.io, 2017.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration spaCy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing.https://spacy.io, 2017

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:07.153129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82113296-4881-479c-97d4-4efb7ea0ddfc · outbound

This paper cites an unresolved cited work.

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

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:07.273218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c78dab6-bffc-45b3-bedd-47f1c28a9ee0 · outbound

This paper cites Neo4j developer documentation.https://neo4j.com/docs/.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Neo4j developer documentation.https://neo4j.com/docs/

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:07.394567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5efd0fe-d147-4c11-adda-0baceecdb467 · outbound

This paper cites calculation-heavy.

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

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:07.489236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1db89169-1ca0-46be-bf58-e681b02abeb2 · outbound

This paper cites an unresolved cited work.

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

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:07.630140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 260d7d6a-e94d-4587-bd1a-7e93b56d5098 · outbound

This paper cites an unresolved cited work.

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

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:07.789061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fb9d08a-ee10-4e01-8988-bc7776214c5b · outbound

This paper cites an unresolved cited work.

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

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:07.906329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9a3a41a-6307-4ff6-96bd-379e3589644e · outbound

This paper cites an unresolved cited work.

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

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:08.029400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bae07f8c-072f-4664-a513-aa5fe10c0cee · outbound

This paper cites [question].

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

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:08.141748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9afe80e-2cbd-43f0-b105-8dd7cbc4ad18 · outbound

This paper cites Formally, a questionq satisfies this criterion if it passes both automated grammar checks and human inspection for clarity and style.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Formally, a questionq satisfies this criterion if it passes both automated grammar checks and human inspection for clarity and style

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:08.289103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e0780f6-9d3c-4a35-96b2-0b0866757c07 · outbound

This paper cites Example of Non-compliance:Which are prime numbers? Options:{2,3,4,5}(with two correct answers: 2 and 3).

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration Example of Non-compliance:Which are prime numbers? Options:{2,3,4,5}(with two correct answers: 2 and 3)

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:08.411822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb504c06-2ccc-4f17-85ff-1aced58ea011 · outbound

This paper cites New York City.

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

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:08.566605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 291b0d91-f596-4de7-9b8f-caf45212c01a · outbound

This paper cites Eiffel Tower.

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

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:08.707496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93bde395-4206-4519-b94f-65bb688f5171 · outbound

This paper cites World History.

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

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:08.881993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afc56dff-5701-44de-b445-5f6e7e9fbf51 · outbound

This paper cites an unresolved cited work.

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

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:08.996374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f56e549-693f-4b54-ae6d-ddcddf94615c · outbound

This paper cites an unresolved cited work.

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

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:09.088498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f95cbbf5-e557-4fbd-baa9-c2a8e097f97f · outbound

This paper cites 31 Only candidates passing all these checks are retained; others are discarded and flagged for human audit by theCuratormodule.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration 31 Only candidates passing all these checks are retained; others are discarded and flagged for human audit by theCuratormodule

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:09.142560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 572b3d37-0df1-4fce-aa68-49ba8b24d4fa · outbound

This paper cites an unresolved cited work.

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

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:09.231805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7dfb183-d9e8-4e17-9326-1fd261c365d9 · outbound

This paper cites an unresolved cited work.

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

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:09.326487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d0fb8e3-b750-47f5-a0ca-7f40f59fbe41 · outbound

This paper cites Second World War.

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

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:09.446970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70942797-2b40-444f-afff-28ebf071541e · outbound

This paper cites moving outward.

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

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:09.522349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63914293-b232-4650-b13a-b69fab3cfe18 · outbound

This paper cites vd traces its origins to which founding entity?.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration vd traces its origins to which founding entity?

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:09.596800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 373a3dad-d071-41e7-8090-d88d98c80f70 · outbound

This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.

KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-02T21:28:05.378047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers