Papers by Alexander Feng
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions (2024.emnlp-main)
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| Challenge: | a new variational approach to distractors in multiple-choice questions is needed . high-quality distractors are crucial to the assessment and pedagogical value of MCQs . a variational method that learns the error behind distractors is more effective . |
| Approach: | They propose a variational approach that learns an interpretable representation of errors behind distractors in math MCQs. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on distractors in math MCQs. |
Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities (N19-1)
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Alexander Erdmann, David Joseph Wrisley, Benjamin Allen, Christopher Brown, Sophie Cohen-Bodénès, Micha Elsner, Yukun Feng, Brian Joseph, Béatrice Joyeux-Prunel, Marie-Catherine de Marneffe
| Challenge: | Scholars in interdisciplinary fields like the Digital Humanities are increasingly interested in semantic annotation of specialized corpora. |
| Approach: | They propose an active learning solution for named entity recognition that maximizes a custom model’s improvement per additional unit of manual annotation. |
| Outcome: | The proposed model reduces required annotation by 20-60% and outperforms a competitive active learning baseline. |
FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information (2023.acl-long)
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| Challenge: | Recent work shows that large language models that have access to state information can generate higher quality game turns than LLMs that use dialog history alone. |
| Approach: | They present a dataset of game play sessions from real D&D gameplay on Discord with true game state info. |
| Outcome: | The proposed model can generate executable Avrae commands, especially after fine tuning. |
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models (2024.findings-naacl)
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Wanyong Feng, Jaewook Lee, Hunter McNichols, Alexander Scarlatos, Digory Smith, Simon Woodhead, Nancy Ornelas, Andrew Lan
| Challenge: | Multiple-choice questions (MCQs) are easy to administer and grade . but crafting high-quality distractors remains labor-intensive and limited scalability . |
| Approach: | They propose to automate the generation of distractors in math MCQs by using large language models to generate distractors. |
| Outcome: | The proposed methods can generate valid distractors, but they are less adept at anticipating common errors or misconceptions among real students. |
A SMART Mnemonic Sounds like “Glue Tonic”: Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick (2024.emnlp-main)
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Nishant Balepur, Matthew Shu, Alexander Hoyle, Alison Robey, Shi Feng, Seraphina Goldfarb-Tarrant, Jordan Boyd-Graber
| Challenge: | a new study shows that mnemonics are not effective at matching student learning to a standardized learning model. |
| Approach: | They build a keyword mnemonic generator that finds mnemonics students favor in a flashcard app . they use expressed and observed preferences to find out what students think is helpful . |
| Outcome: | The proposed mnemonics outperform existing models in keyword mnemonics . the human writer outperformed both models in terms of keyword simplicity and explanation quality . |