Papers with feedback generation
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. |
Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision (2024.naacl-long)
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| Challenge: | Recent studies have conjectured that multimodal hallucination is due to the vision encoder failing to ground on the image properly. |
| Approach: | They propose a multimodal self-feedback guided revision model that leverages visual cues to generate feedback to its initial response based on the visual information provided by the vision encoder. |
| Outcome: | The proposed model reduces multimodal hallucination and outperforms previous models on MMHal-Bench, POPE, and GAVIE. |
Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflection (2025.acl-long)
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Cilin Yan, Jingyun Wang, Lin Zhang, Ruihui Zhao, Xiaopu Wu, Kai Xiong, Qingsong Liu, Guoliang Kang, Yangyang Kang
| Challenge: | Recent work utilizes feedbacks generated from erroneous cases to guide prompt optimization . previous methods rely on computational resources and powerful GPUs . |
| Approach: | They propose an automatic prompt engineering method that leverages feedbacks from erroneous cases to guide prompt optimization. |
| Outcome: | The proposed method surpasses state-of-the-art methods with less steps and lower computational resources. |
MathEDU: Feedback Generation on Problem-Solving Processes for Mathematical Learning Support (2026.eacl-long)
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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. |
| Approach: | They propose to use a dataset to evaluate the reliability of large language models in mathematics and a teacher-written feedback system to improve student problem-solving processes. |
| Outcome: | The proposed model improves in correctness classification, error identification, and feedback generation, but generates a gap from teacher-written feedback. |
IDEAlign: Comparing Ideas of Large Language Models to Domain Experts (2026.eacl-long)
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| Challenge: | Large language models are increasingly used to produce open-ended, interpretive annotations. |
| Approach: | They propose to use LLM annotations to evaluate content and assess expert similarity . they propose to benchmark different similarity methods against human ratings . |
| Outcome: | The proposed method performs best but falls short of expert alignment . it is useful as a triage filter rather than a substitute for human review. |
CEAES: Bidirectional Reinforcement Learning Optimization for Consistent and Explainable Essay Assessment (2025.acl-long)
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| Challenge: | Current automated essay quality assessment systems treat score prediction and feedback generation as separate tasks. |
| Approach: | They propose a bidirectional reinforcement learning framework that jointly optimizes score prediction and feedback generation. |
| Outcome: | The proposed framework outperforms current state-of-the-art models in both scoring and feedback quality. |