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Andrew Lan

As seen in: arXiv
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Articles by Andrew Lan

Computer Science > Software Engineering

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By Tongping Liu, Andrew Lan, Vinodh Kumaran Jayakumar
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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gencat: Generative computerized adaptive testing

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By Andrew Lan
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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RADAR: Reasoning-Ability and Difficulty-Aware Routing for Reasoning LLMs

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By Branislav Kveton, Ryan Rossi, Andrew Lan, Zichao Wang
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2507.06910] Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues

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By Alexander Scarlatos, Andrew Lan
| arXiv
[Submitted on 9 Jul 2025] Title:Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues View a PDF of the paper titled Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues, by Fareya Ikram and 2 other authors View PDF HTML (experimental) Abstract:Tutoring dialogues have gained significant attention in recent years, given the prominence of online learning and the emerging tutoring abilities of artificial intelligence (AI) agents powered by large...
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[2502.19407] Learning Code-Edit Embedding to Model Student Debugging Behavior

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By Andrew Lan
| arXiv
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[2411.05231] Evaluating GPT-4 at Grading Handwritten Solutions in Math Exams

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By Andrew Lan, Alexander Scarlatos
| arXiv
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[2405.00302] Generating Feedback-Ladders for Logical Errors in Programming using Large Language Models

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By Andrew Lan
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2405.05144] Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank

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By Andrew Lan, Wanyong Feng, Digory Smith, Simon Woodhead
| arXiv
arXiv:2405.05144 (cs) View PDF Comments: Subjects: Computers and Society (cs.CY); Machine Learning (cs.LG) Cite as: arXiv:2405.05144 [cs.CY] (or arXiv:2405.05144v1 [cs.CY] for this version) Submission history From: Wanyong Feng [ view email] [v1] Fri, 19 Apr 2024 00:25:44 UTC (7,950 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Links...
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RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning

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By Andrew Lan
| arXiv
[Submitted on 23 May 2023 ( v1 ), last revised 16 Apr 2024 (this version, v2)] Title:RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning View a PDF of the paper titled RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning, by Alexander Scarlatos and Andrew Lan View PDF HTML (experimental) Abstract:Recent developments in large pre-trained language models have enabled unprecedented performance on a variety of downstream tasks.
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SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues

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By Andrew Lan
| arXiv
In second language vocabulary learning, existing works have primarily focused on either the learning interface or scheduling personalized retrieval practices to maximize memory retention. However, the learning content, i.e., the information presented on flashcards, has mostly remained constant.
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