Papers with distillation framework
BayesKD: Bayesian Knowledge Distillation for Compact LLMs in Constrained Fine-tuning Scenarios (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have revolutionized various domains with their remarkable capabilities, but their massive parameter sizes pose significant challenges for fine-tuning and inference. |
| Approach: | They propose a Bayesian Knowledge Distillation framework for compact Large Language Models in resource-constrained fine-tuning scenarios that employs Logits Dual-Scaling, Knowledge Alignment Module, and Bayes Distillations Optimization. |
| Outcome: | The proposed framework outperforms baseline methods on various state-of-the-art LLMs, including LLaMA, Qwen2, Bloom, and Vicuna. |
Topic-Regularized Authorship Representation Learning (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing techniques for authorship attribution have focused on out-of-distribution in topics or authors. |
| Approach: | They propose a framework that creates authorship representation with reduced reliance on topic-specific information to handle a large number of unseen authors and topics. |
| Outcome: | The proposed framework has improved over baselines in 4 out of 6 cases. |
CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning (2024.acl-long)
Copied to clipboard
Weiqi Wang, Tianqing Fang, Chunyang Li, Haochen Shi, Wenxuan Ding, Baixuan Xu, Zhaowei Wang, Jiaxin Bai, Xin Liu, Cheng Jiayang, Chunkit Chan, Yangqiu Song
| Challenge: | Existing approaches to generalize commonsense reasoning lack instantiated knowledge and require pre-built concept taxonomies and annotations. |
| Approach: | They propose a framework that iteratively performs contextualized conceptualization and instantiation over commonsense knowledge bases by instructing large language models to generate both types of knowledge with critic filtering. |
| Outcome: | Empirical results show that distilling CANDLE on student models provides benefits across three downstream tasks. |
uDistil-Whisper: Label-Free Data Filtering for Knowledge Distillation in Low-Data Regimes (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent work on distilling Whisper’s knowledge into small models using pseudo-labels shows promising performance while reducing the size by up to 50%. |
| Approach: | They propose a framework that distills Whisper’s knowledge into small models using pseudo-labels and reduces the size by up to 50%. |
| Outcome: | The proposed model outperforms the teacher model by 5-7 WER points and is 25-50% more efficient when scaling the data. |
MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)
Copied to clipboard
Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song
| Challenge: | Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability. |
| Approach: | They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones. |
| Outcome: | The proposed framework shows that it is robust to different prompts and superior to previous methods. |
ConGen: Unsupervised Control and Generalization Distillation For Sentence Representation (2022.findings-emnlp)
Copied to clipboard
Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Lalita Lowphansirikul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Sentence representations are essential in many NLP tasks operating at the sentence level. |
| Approach: | They propose an unsupervised sentence representation method to reduce the supervised-unsupervised performance gap for smaller models. |
| Outcome: | The proposed method outperforms supervised training on STS, text classification, and natural language inference tasks on smaller models. |
AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models (2026.findings-acl)
Copied to clipboard
Feng Luo, Yu-Neng Chuang, Guanchu Wang, Hoang Anh Duy Le, Shaochen Zhong, Hongyi Liu, Jiayi Yuan, Yang Sui, Vladimir Braverman, Vipin Chaudhary, Xia Hu
| Challenge: | Existing approaches to distilling large language models (LLMs) are inefficient and generate excessively long chain-of-thought reasoning even for inputs that admit concise solutions. |
| Approach: | They propose a distillation framework that empowers non-reasoning LLMs to think only when necessary. |
| Outcome: | The proposed framework reduces reasoning length up to 71% with minimal accuracy loss while preserving accuracy. |
Experience is the Teacher: Reusing Atomic Thoughts from LLMs to Improve Medical Dialogue (2026.findings-acl)
Copied to clipboard
Guangya Yu, Hui Luo, Qi Ye, Ruihui Hou, Weiyan Zhang, Mingxi Shang, Xuanwu Li, ChunMing Wang, Tong Ruan
| Challenge: | Recent large reasoning models (LLMs) lack dynamic and diverse thinking capabilities . reusing atomic thoughts provides a practical pathway toward dynamic reasoning . |
| Approach: | They propose a framework that extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses. |
| Outcome: | The proposed framework extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses. |
Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent efforts to develop lightweight and practical sentiment analysis models are limited by manual instruction and large-scale user texts. |
| Approach: | They propose a framework for sentiment analysis that uses attribute-based instruction construction and difficulty-based data filtering to distill knowledge. |
| Outcome: | The proposed framework outperforms baseline methods in data efficiency and performance. |
Probe Then Retrieve and Reason: Distilling Probing and Reasoning Capabilities into Smaller Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent research efforts have focused on distilling Large Language Models into Small Language Model (SLMs) however, the results of CoT distillation are inadequate for knowledge-intensive reasoning tasks. |
| Approach: | They propose a retrieval-based framework which distills question probing and reasoning capabilities from Large Language Models into SLMs. |
| Outcome: | The proposed framework improves probing and reasoning capabilities of large language models in knowledge-intensive reasoning tasks. |
Do LLMs Encode Functional Importance of Reasoning Tokens ? (2026.acl-long)
Copied to clipboard
| Challenge: | Existing compact reasoning approaches generate long reasoning chains, but they lack a mechanism to encode token-level functional importance for answer generation. |
| Approach: | They propose a procedure that iteratively removes reasoning tokens from models and prunes them to yield length-controlled reasoning chains. |
| Outcome: | The proposed procedure outperforms a frontier model at reasoning lengths and shows that attention scores predict greedy pruning ranks. |