Papers by Andrew 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. |
Interpretable Math Word Problem Solution Generation via Step-by-step Planning (2023.acl-long)
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| Challenge: | Existing approaches to solving math word problems focus on obtaining the correct answer. |
| Approach: | They propose a step-by-step planning approach for intermediate solution generation that strategically plans the generation of the next solution step based on the MWP and the previous solution steps. |
| Outcome: | The proposed approach improves the accuracy and interpretability of the solution on automatic metrics and human evaluation. |
AlignBench: Benchmarking Chinese Alignment of Large Language Models (2024.acl-long)
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Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Andrew Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Xiaotao Gu, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang
| Challenge: | Effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluations tailored for alignment. |
| Approach: | They propose a multi-dimensional benchmark for evaluating LLMs’ alignment in Chinese with 8 main categories, 683 real-scenario rooted queries and corresponding human verified references. |
| Outcome: | The benchmark uses a human-in-the-loop data curation pipeline, 683 real-scenario rooted queries and human verified references. |
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. |
Masked Diffusion Captioning for Visual Feature Learning (2025.findings-emnlp)
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| Challenge: | Unlike autoregressive captioning, the strength of the visual learning signal in MDC does not depend on each token’s position in the sequence, reducing the need for auxiliary objectives. |
| Approach: | a decoder conditioned on visual features is trained to reconstruct the original text. |
| Outcome: | masked diffusion captioning (MDC) is a form of image-conditioned captioning that can be applied to visual tasks. |
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. |
CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation (2024.acl-long)
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Pei Ke, Bosi Wen, Andrew Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Existing models for NLP evaluations lack the ability to generate informative critiques in pointwise grading and pairwise comparison especially without references. |
| Approach: | They propose a method which can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting to obtain informative evaluation data in different tasks and settings. |
| Outcome: | The proposed method outperforms all open-source models and even GPT-4 in system-level correlations of pointwise grading. |