Closing the Loop: Learning to Generate Writing Feedback via Language Model Simulated Student Revisions (2024.emnlp-main)
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| Challenge: | Recent advances in language models (LMs) have made it possible to automatically generate feedback that is actionable and well-aligned with human-specified attributes. |
| Approach: | They propose a tool that PROduces Feedback via learning from LM simulated student revisions and propose to iteratively optimize the feedback generator by directly maximizing the effectiveness of students’ overall revising performance. |
| Outcome: | The proposed approach surpasses baseline methods in effectiveness of improving students’ writing and demonstrates enhanced pedagogical values, even though it was not explicitly trained for this aspect. |
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| Challenge: | Existing work on feedback comment generation has been limited . despite its usefulness, there is no publicly available dataset for research on feedback comments . |
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| Challenge: | Existing studies have examined the reliability of Large Language Models (LLMs) in grading authentic student problem solving processes and delivering effective feedback. |
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Exploring Methods for Generating Feedback Comments for Writing Learning (2021.emnlp-main)
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| Challenge: | Existing methods for generating explanatory notes for language learners are inadequate . nagata et al. demonstrates that neural-retrieval-based methods can generate feedback comments for preposition use . |
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A Dataset for Investigating the Impact of Feedback on Student Revision Outcome (2020.lrec-1)
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| Challenge: | Despite numerous studies on the kinds of feedback that can best promote learning, this question remains an open debate in the area of Second Language Acquisition (SLA). |
| Approach: | They annotate a corpus of student-written sentences with teacher feedback provided for the errors. |
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The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Values (2023.emnlp-main)
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| Challenge: | Incorporating human feedback into Large Language Models is a welcome development, but it introduces new biases and challenges. |
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LLMCrit: Teaching Large Language Models to Use Criteria (2024.findings-acl)
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| Challenge: | Current research on using criteria to provide feedback on tasks is limited . a general framework that can be used to teach large language models to use criteria is lacking . |
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| Challenge: | Current models provide specific and mostly accurate writing feedback, but they fail to identify the biggest writing issue in the story and to correctly decide when to offer critical vs. positive feedback. |
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Through the Looking Glass: Learning to Attribute Synthetic Text Generated by Language Models (2021.eacl-main)
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| Challenge: | Recent advances in natural language processing have enabled synthetic text generation that is often comparable to the organic text. |
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FEAT: A Preference Feedback Dataset through a Cost-Effective Auto-Generation and Labeling Framework for English AI Tutoring (2025.acl-short)
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| Challenge: | Existing algorithms for teacher feedback generation are time-consuming and costly to generate manually. |
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Evaluating Language Models as Synthetic Data Generators (2025.acl-long)
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Seungone Kim, Juyoung Suk, Xiang Yue, Vijay Viswanathan, Seongyun Lee, Yizhong Wang, Kiril Gashteovski, Carolin Lawrence, Sean Welleck, Graham Neubig
| Challenge: | Prior studies have focused on developing effective data generation methods, but lack systematic comparison of different LMs as data generators in a unified setting. |
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