| Challenge: | In this paper, we explore the task of instructional dialogue and focus on the cooking domain. |
| Approach: | They propose to explore two auxiliary subtasks to support response generation with improved instruction grounding by incorporating user intent and instruction state information into the model. |
| Outcome: | The proposed model lacks understanding of user intent and inability to track instruction state (i.e., which step was last instructed) incorporating user intent information helps the response generation model mitigate the incorrect order issue. |
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| Challenge: | generative AI is expanding in education, yet empirical analyses of large-scale and real-world interactions between students and AI systems remain limited. |
| Approach: | They present a dataset based on a semester-long experiment with 212 college students in English as Foreign Language (EFL) writing courses. |
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ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity? (2023.acl-short)
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Michael Heck, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Shutong Feng, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Milica Gasic
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Enhancing Chat Language Models by Scaling High-quality Instructional Conversations (2023.emnlp-main)
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| Challenge: | a recent study validates the effectiveness of chat language models by fine-tuning instruction data. |
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| Challenge: | Understanding procedural texts is essential for enabling machines to follow instructions and reason about tasks. |
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| Challenge: | generative models struggle to distinguish subtle differences among retrieved knowledge records, resulting in suboptimal quality of generated responses. |
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A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks (2020.acl-main)
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| Challenge: | a web-based algorithm can be used to align instructions for different tasks . video instructions can be noisy and contain far more information than textual instructions. |
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Towards LLM-driven Dialogue State Tracking (2023.emnlp-main)
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PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)
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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang
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Recipe Instruction Semantics Corpus (RISeC): Resolving Semantic Structure and Zero Anaphora in Recipes (2020.aacl-main)
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| Challenge: | Existing approaches to understanding recipe instructions make assumptions that are domain specific. |
| Approach: | They propose a new dataset for information extraction on recipes . they avoid a priori pre-defining domain-specific predicates to recognize . instead, they focus on basic understanding of the expressed semantics . |
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