Papers by Rose Wang
Problem-Oriented Segmentation and Retrieval: Case Study on Tutoring Conversations (2024.findings-emnlp)
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| Challenge: | POSR is a task of breaking down conversations into segments and linking each segment to the relevant reference item. |
| Approach: | They propose a task that breaks down conversations into segments and links each segment to the relevant reference item. |
| Outcome: | The proposed method outperforms independent segmentation pipelines and large language models on joint metrics. |
Let’s Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought (2023.emnlp-main)
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Vaishnavi Himakunthala, Andy Ouyang, Daniel Rose, Ryan He, Alex Mei, Yujie Lu, Chinmay Sonar, Michael Saxon, William Wang
| Challenge: | Existing studies show vision-language systems can reason about images using natural language, but their capacity for video reasoning remains underexplored. |
| Approach: | They propose to frame video reasoning as the sequential understanding of a small number of keyframes, thereby leveraging the power and robustness of vision-language systems' capacity to reason about images using natural language. |
| Outcome: | The proposed models can generate multiple intermediate keyframes and predict future keyframe, and they perform poorly on GPT-4, GPT-3, and VICUNA. |
Generation, Distillation and Evaluation of Motivational Interviewing-Style Reflections with a Foundational Language Model (2024.eacl-long)
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| Challenge: | Motivational Interviewing (MI) is a counselling technique used to guide people towards behaviour change. |
| Approach: | They propose a method for distilling reflections from a foundational language model into smaller models that can be owned and controlled. |
| Outcome: | The proposed method achieves 100% success rate on hold-out test set and 90% on the GPT-2 XL. |
Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes (2024.naacl-long)
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| Challenge: | Our work explores the potential of large language models (LLMs) to close the novice-expert knowledge gap in remediating math mistakes. |
| Approach: | They propose a method that uses cognitive task analysis to translate an expert’s latent thought process into a decision-making model for remediation. |
| Outcome: | The proposed model can bridge the novice-expert knowledge gap by using cognitive task analysis to translate an expert’s latent thought process into a decision-making model for remediation. |
Backtracing: Retrieving the Cause of the Query (2024.findings-eacl)
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| Challenge: | a number of online content portals allow users to ask questions to supplement their understanding. |
| Approach: | They propose a task of backtracing to retrieve the text segment that most likely caused a user query. |
| Outcome: | The proposed method improves on the backtracing task in three domains . the results show that there is room for improvement and new retrieval approaches . |
MathFish: Evaluating Language Model Math Reasoning via Grounding in Educational Curricula (2024.findings-emnlp)
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| Challenge: | pedagogical experts spend months reviewing published math problems to ensure that they align with critical skills or concepts. |
| Approach: | They propose a novel approach for evaluating language models' mathematical abilities by combining a dataset of 385 fine-grained descriptions of K-12 math skills and concepts with 9.9K math problems labeled with these standards. |
| Outcome: | The proposed model can discern skills and concepts enabled by math content, and it can be used to assess language models' mathematical abilities. |
“Mistakes Help Us Grow”: Facilitating and Evaluating Growth Mindset Supportive Language in Classrooms (2023.emnlp-main)
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| Challenge: | GMSL has been shown to significantly reduce disparities in academic achievement and enhance students’ learning outcomes. |
| Approach: | They develop a coaching tool to reframe unsupportive utterances to GMSL using large language models. |
| Outcome: | The proposed model outperforms the GMSL-trained teachers in fostering a growth mindset and promoting challenge-seeking behavior. |
Improving Model Factuality with Fine-grained Critique-based Evaluator (2025.acl-long)
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Yiqing Xie, Wenxuan Zhou, Pradyot Prakash, Di Jin, Yuning Mao, Quintin Fettes, Arya Talebzadeh, Sinong Wang, Han Fang, Carolyn Rose, Daniel Fried, Hejia Zhang
| Challenge: | Factuality evaluation aims to detect factual errors produced by language models and guide the development of more factual models. |
| Approach: | They propose a framework that leverages FenCE to improve the factuality of LM generators by constructing training data. |
| Outcome: | The proposed framework improves the factuality of LM generators by enhancing their training data. |
Calibrate your listeners! Robust communication-based training for pragmatic speakers (2021.findings-emnlp)
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| Challenge: | Prior work has investigated training NLP systems with communication-based objectives . prior work has focused on supervised learning, but is expensive to collect . |
| Approach: | They propose a method that uses a population of neural listeners to regularize speaker training. |
| Outcome: | The proposed method improves on ensemble- and dropout-based listening populations on reference games and generalizes to new games and listeners. |
Edu-ConvoKit: An Open-Source Library for Education Conversation Data (2024.naacl-demo)
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| Challenge: | Edu-ConvoKit is an open-source library for analyzing education conversation data. |
| Approach: | They introduce Edu-ConvoKit, an open-source library for conversation data analysis. |
| Outcome: | The open-source library handles pre-processing, annotation and analysis of education conversation data. |
Incorporating Multimodal Information in Open-Domain Web Keyphrase Extraction (2020.emnlp-main)
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| Challenge: | Open-domain Keyphrase extraction (KPE) is a fundamental yet complex NLP task . effective designs encode within layout and formatting signals that point to where the important information can be found. |
| Approach: | They propose a multi-modal approach to open-domain keyphrase extraction (KPE) on the Web that leverages layout and formatting signals to aid in the task. |
| Outcome: | The proposed model outperforms state-of-the-art models on the open-domain keyphrase extraction task. |