Challenge: Existing studies have shown that curriculum learning facilitates dialogue generation tasks while knowledge distillation can yield significant performance boosts for student models.
Approach: They propose a combination of curriculum learning and knowledge distillation for dialogue generation models . they cluster training cases according to their complexity and employ an adversarial training strategy .
Outcome: The proposed model improves compared with baselines.

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Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings (D19-61)

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Challenge: Recent research points to knowledge distillation as a potential solution for NLU tasks.
Approach: They propose a training approach that distills large finetuned LMs into a small network using unlabeled training examples.
Outcome: The proposed approach outperforms BERT training approaches while using 300 times fewer parameters.
Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection (2022.findings-emnlp)

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Challenge: Existing studies on knowledge distillation have shown that not all knowledge is necessary for learning a good student model.
Approach: They propose an actor-critic approach to selecting appropriate knowledge to transfer during the process of knowledge distillation.
Outcome: The proposed method outperforms several strong knowledge distillation baselines significantly on the GLUE datasets.
Diversifying Neural Dialogue Generation via Negative Distillation (2022.naacl-main)

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Challenge: Existing approaches to generate generic responses are ignoring low-frequency but generic responses and bringing low- frequency but meaningless responses.
Approach: They propose a negative training paradigm that reminds dialogue models not to generate high-frequency responses during training.
Outcome: The proposed method outperforms previous methods in the generic response problem while minimizing low-frequency but meaningless responses.
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data (2020.emnlp-main)

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Challenge: Existing research has focused on training open-domain dialogue models using unpaired data.
Approach: They propose a data-level distillation method for training open-domain dialogue models by utilizing unpaired data.
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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.
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On Knowledge distillation from complex networks for response prediction (N19-1)

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Challenge: Recent advances in Question Answering have led to the development of very complex models . however, these models are expensive in space and time and require limited resources .
Approach: They propose to use simple models which learn to emulate characteristics of a teacher network . they use a 12GB Tesla K80 GPU to restrict the maximum length of the input document .
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DistillCSE: Distilled Contrastive Learning for Sentence Embeddings (2023.findings-emnlp)

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Challenge: Existing approaches to sentence embeddings are based on contrastive learning (CL) .
Approach: They propose a framework which performs contrastive learning under the self-training paradigm with knowledge distillation and propose 'Group-P shuffling strategy' and averaging logits from multiple teacher components.
Outcome: The proposed framework outperforms many strong baseline methods and yields a new state-of-the-art performance.
Calibrated Progressive Distillation: Co-Designing Curriculum and Target Mixing for Knowledge Distillation of Large Language Models (2026.findings-acl)

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Challenge: Existing methods for knowledge distillation address the teacher–student capacity gap by mixing teacher and student distributions in the distillation target or using curriculum learning to sequence training from easy to hard examples.
Approach: They propose a white-box KD framework that co-designs curriculum scheduling and target mixing through a unified difficulty-aware principle.
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AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression (2023.acl-long)

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Challenge: Existing knowledge distillation methods focus on the transfer of model-specific knowledge but overlook data-specific information.
Approach: They propose an attribution-driven knowledge distillation approach which explores the token-level rationale behind the teacher model and transfers attribution knowledge to the student model.
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Tutoring Helps Students Learn Better: Improving Knowledge Distillation for BERT with Tutor Network (2022.emnlp-main)

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Challenge: Existing knowledge distillation approaches for language models have overlooked the difficulty of training examples.
Approach: They propose a framework that controls difficulty of training examples during pre-training by a tutor network.
Outcome: The proposed framework outperforms state-of-the-art KD methods with student models on the GLUE benchmark.

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