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总结下面文字材料：


Distillation Techniques for Enhancing SLM Performance
Following the discussion on data quality in KD, this section reviews specialized KD training strategies designed to
enhance the performance of SLMs. The motivation is to address the unique challenges and constraints involved in
distilling knowledge from LLMs to SLMs, ensuring that the smaller models can maximize their performance gains. As
shown in Figure 11, two main gaps between LLMs and SLMs lead to challenges in distillation: distribution mismatch and
domain gap. Distribution mismatch [6, 155] occurs when the distribution of output sequences during training does not
align with the distribution of sequences that SLMs produce during inference, leading to suboptimal performance of the
student model. The domain gap [362] arises when there is a discrepancy between the domains or tasks on which the
LLMs and SLMs are trained and applied. This gap can cause significant degradation in the performance of the student
model if not properly addressed during the distillation process. To address these issues, specialized strategies involve
first aligning the teacher and student models with the target domain before proceeding with knowledge distillation. To
explore these challenges further, we now delve into the details of these two branches of methods.
Distribution Mismatch In original knowledge distillation, illustrated in Figure 11 Distribution Mismatch (a),
the teacher and student are provided with the same input sequences 𝑥 and output labels 𝑦, producing probability
distributions for the next token (𝑞 and 𝑝). The loss is calculated as the difference between these two distributions,
𝐷(𝑞, 𝑝). However, a key challenge arises due to distribution mismatch: the output sequences during training (𝑦) differ in
distribution from those the SLMs produce during inference (𝑦
′
). To address this challenge, various techniques have been
proposed. As shown in Figure 11 Distribution Mismatch (b), one approach trains the student model using on-policy
sequences—sequences generated by the student itself—guided by the teacher model’s feedback. Specifically, both the
student and teacher take the same input (𝑥) and the student-generated output (𝑦
′
), producing probability distributions
for the next token (𝑞 and 𝑝, respectively). The loss is calculated as the difference between these two distributions,
𝐷(𝑞, 𝑝). This approach helps the student model reduce the distribution gap between training and inference by learning
from the teacher’s feedback on its own generated sequences. Generalized Knowledge Distillation (GKD) [6] is the
first work using this technique and improves distillation outcomes. However, a drawback of this technique is that
it requires the student to constantly produce new training sequences, which can be computationally expensive. To
improve efficiency, as shown in Figure 11 Distribution Mismatch (c), an adaptive off-policy approach can be used to
efficiently manage student-generated outputs by storing them in a replay buffer, thereby reducing computational costs.
DistiLLM [155] employs this off-policy approach and improves the efficiency of KD
Domain Gap When training an SLM in a specific domain that differs from the domain of the LLMs, the gap between
the two domains becomes problematic. As illustrated in Figure 11 Domain Gap (a), domain adaptation fine-tunes a
language model, initially trained on a general corpus, using a specialized dataset such as PubMed to enhance performance
in that specific domain. As illustrated in Figure 11 Domain Gap (b), Knowledge distillation transfers knowledge from
the larger model to the smaller one. However, because the teacher model may not produce high-quality outputs on
specialized datasets, domain adaptation is needed prior to knowledge distillation. As illustrated in Figure 11 Domain
Gap (c), Adapt-and-Distill [362] tackles the domain gap by distilling general large models into smaller ones. This
paper introduces AdaLM and demonstrates that the “Adapt-and-Distill” strategy—first involving domain adaptation
of both the large teacher model and the small student model, followed by distillation—is the most effective compared
to three other strategies: training directly from scratch, distillation followed by adaptation, and adapting the teacher
model before distillation into a general small student model. These innovative techniques are crucial for enhancing the
capabilities of SLMs, making them more efficient and effective for various applications. However, adapting both the
teacher (LLMs) and the student (SLMs) models to the target domain can be time-consuming. Future research could
focus on efficiently solving the domain gap problem.
Insights: Here are some insights from distillation techniques:
• Sampling SLM outputs during the training process is the main approach to resolving distribution mismatch.
• Techniques like Adapt-and-Distill address the domain gap by first adapting both the teacher (LLMs) and the
student (SLMs) models to the target domain before proceeding with distillation.


[ASSISTANT]

提升SLM性能的蒸馏技术

为了应对将知识从大语言模型（LLM）蒸馏到小语言模型（SLM）中的特定挑战，专门的知识蒸馏（KD）策略聚焦于通过克服两个主要差距来提升SLM的性能：**分布不匹配**和**领域差距**。

1. **分布不匹配**：这种差距发生在训练期间的输出序列分布与SLM在推理时生成的序列分布不一致，可能导致学生模型性能不佳。
   - **基于政策的序列训练**：学生模型在训练时生成自己的序列，并接受教师模型的反馈，以减少训练与推理之间的分布差距。**Generalized Knowledge Distillation (GKD)** 引入了这种方法，通过让学生模型在自己的生成序列上进行学习来改善蒸馏效果。然而，这种方法计算成本较高，因为学生模型需要持续生成新的训练序列。
   - **自适应离政策方法**：为了提高效率，可以使用重放缓冲区存储学生生成的输出序列，从而减少计算成本。**DistiLLM** 就使用了这一方法，提升了蒸馏效率。

2. **领域差距**：当SLM训练的特定领域与LLM的领域不同时，这种差距会对学生模型的性能产生影响。
   - **领域适应**：在蒸馏之前，使用特定领域的数据集（如PubMed）对模型进行微调，以提升其在该领域的表现。
   - **Adapt-and-Distill**：该方法先对教师模型和学生模型进行领域适应，再进行知识蒸馏。实验表明，与直接训练、先蒸馏再适应以及先适应教师再蒸馏等策略相比，Adapt-and-Distill方法更为有效。然而，这种方法也较为耗时，因此未来的研究可以集中在如何高效地解决领域差距问题上。

**关键见解**：
- 通过训练过程中的SLM输出采样，是解决分布不匹配的主要方法。
- 像Adapt-and-Distill这样的技术，通过先对教师（LLM）和学生（SLM）模型进行领域适应，然后再进行蒸馏，来有效解决领域差距问题。



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