Papers with feedback generator
Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios (2023.emnlp-industry)
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| Challenge: | Existing table-to-text generation techniques that transform complex tabular data into comprehensible narratives are lacking in real-world applications. |
| Approach: | They investigate the table-to-text capabilities of different LLMs using four datasets within two real-world information seeking scenarios. |
| Outcome: | The proposed models can generate table-to-text data in two real-world information seeking scenarios and perform better than existing models. |
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. |
RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation (2026.findings-acl)
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| Challenge: | Prior studies show that large language models can draft fluent reviews but they miss specific issues, show shallow analysis, and produce generic phrasing. |
| Approach: | They propose a task that targets actionable review feedback generation and places existing peer review rebuttal at the center of learning. |
| Outcome: | The proposed model improves on a large dataset that maps review segments to rebuttal segments that address them, with perspective labels and impact categories that order author uptake. |