# Collaborative Distillation Strategies for Parameter-Efficient Language Model Deployment

arXiv: https://arxiv.org/abs/2507.15198 · HTML: https://mlsanity.com/paper/2507.15198/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge distillation for LLMs, one of 12 directions in Knowledge distillation.
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Placed in

- Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/)

## Builds on (most cited first, 13 of 13)

- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://mlsanity.com/paper/2004.02984/) (2020)
- [Decoupled Knowledge Distillation](https://mlsanity.com/paper/2203.08679/) (2022)
- [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024)
- [Compact Language Models via Pruning and Knowledge Distillation](https://mlsanity.com/paper/2407.14679/) (2024)
- [Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application](https://mlsanity.com/paper/2407.01885/) (2024)
- [Dynamic Knowledge Distillation for Pre-trained Language Models](https://mlsanity.com/paper/2109.11295/) (2021)
- [DDK: Distilling Domain Knowledge for Efficient Large Language Models](https://mlsanity.com/paper/2407.16154/) (2024)
- [Structured Memory Mechanisms for Stable Context Representation in Large Language Models](https://mlsanity.com/paper/2505.22921/) (2025)
- [Context-Guided Dynamic Retrieval for Improving Generation Quality in RAG Models](https://mlsanity.com/paper/2504.19436/) (2025)
- [Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning](https://mlsanity.com/paper/2504.19583/) (2025)
- [Structured Gradient Guidance for Few-Shot Adaptation in Large Language Models](https://mlsanity.com/paper/2506.00726/) (2025)

## Built on by (most cited first, 5 of 5)

- [Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Models](https://mlsanity.com/paper/2509.03057/) (2025)
- [Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2508.14503/) (2025)
- [Semantic and Structural Analysis of Implicit Biases in Large Language Models: An Interpretable Approach](https://mlsanity.com/paper/2508.06155/) (2025)
- [Graph Neural Network and Transformer Integration for Unsupervised System Anomaly Discovery](https://mlsanity.com/paper/2508.09401/) (2025)
- [Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessment](https://mlsanity.com/paper/2508.09399/) (2025)

## Abstract (arXiv)

This paper addresses the challenges of high computational cost and slow inference in deploying large language models. It proposes a distillation strategy guided by multiple teacher models. The method constructs several teacher models and integrates their output probability distributions and intermediate semantic features. This guides the student model to learn from multiple sources of knowledge. As a result, the student model gains stronger language understanding and generation ability while maintaining a small parameter size. To achieve this, the paper introduces a weighted output fusion mechanism, a feature alignment loss function, and an entropy-driven dynamic teacher weighting strategy. These components improve the quality and stability of knowledge transfer during distillation. Under multi-teacher guidance, the student model captures semantic information more effectively and demonstrates strong performance across multiple evaluation metrics. In particular, the method shows high consistency in expression, generalization ability, and task adaptability in tasks such as language modeling, text generation, and multi-task learning. The experiments compare the proposed method with several widely adopted distillation approaches. The results further confirm its overall advantages in perplexity, distillation loss, and generation quality. This study provides a feasible technical path for the efficient compression of large-scale language models. It also demonstrates the effectiveness of multi-teacher collaborative mechanisms in complex language modeling tasks.