Papers by Suyang Zhu

3 papers
Meta-Reflection: A Feedback-Free Reflection Learning Framework (2025.acl-long)

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Challenge: Existing approaches to improve large language models' ability to understand and reason are limited by external feedback.
Approach: They propose a feedback-free reflection mechanism that requires only a single inference pass without external feedback.
Outcome: The proposed method is based on an industrial e-commerce benchmark and public datasets.
One-Teacher and Multiple-Student Knowledge Distillation on Sentiment Classification (2022.coling-1)

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Challenge: Existing knowledge distillation models require large computing resources and long inference time to perform.
Approach: They propose a one-teacher and multiple-student knowledge distillation approach to distill a deep pre-trained teacher model into multiple shallow student models with ensemble learning.
Outcome: The proposed method achieves better results with fewer parameters and extremely high speedup ratios on three sentiment classification tasks.
Adversarial Attention Modeling for Multi-dimensional Emotion Regression (P19-1)

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Challenge: Empirical evaluation on EMOBANK corpus shows that our approach achieves notable improvements in r-values over the state-of-the-art baselines.
Approach: They propose a neural network-based approach to multi-dimensional emotion regression which automatically rates multiple emotion dimension scores for an input text.
Outcome: The proposed approach achieves notable improvements in r-values on both EMOBANK Reader’s and Writer’s multi-dimensional emotion regression tasks over the state-of-the-art baselines.

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