# Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategies arXiv: https://arxiv.org/abs/2402.09282 ยท HTML: https://mlsanity.com/paper/2402.09282/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Placed in Knowledge distillation for LLMs, one of 12 directions in Knowledge distillation. - Builds on 31 of its 44 references that are in the map. - First posted on arXiv 14 Feb 2024. ## Placed in - Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/) ## Builds on (most cited first, 30 of 31) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022) - [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019) - [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019) - [Large Language Models Are Reasoning Teachers](https://mlsanity.com/paper/2212.10071/) (2022) - [GPT-NER: Named Entity Recognition via Large Language Models](https://mlsanity.com/paper/2304.10428/) (2023) - [UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition](https://mlsanity.com/paper/2308.03279/) (2023) - [Empirical Study of Zero-Shot NER with ChatGPT](https://mlsanity.com/paper/2310.10035/) (2023) - [xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning](https://mlsanity.com/paper/2401.07037/) (2024) - [PromptNER: Prompting For Named Entity Recognition](https://mlsanity.com/paper/2305.15444/) (2023) - [Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task](https://mlsanity.com/paper/2111.02086/) (2021) - [POLYGLOT-NER: Massive Multilingual Named Entity Recognition](https://mlsanity.com/paper/1410.3791/) (2014) - [Empower Sequence Labeling with Task-Aware Neural Language Model](https://mlsanity.com/paper/1709.04109/) (2017) - [LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation](https://mlsanity.com/paper/2210.15461/) (2022) - [GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator](https://mlsanity.com/paper/2212.10218/) (2022) - [CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation](https://mlsanity.com/paper/2210.07022/) (2022) - [mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning](https://mlsanity.com/paper/2308.09073/) (2023) - [FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models](https://mlsanity.com/paper/2308.00065/) (2023) - [UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translation](https://mlsanity.com/paper/2207.04900/) (2022) - [GTrans: Grouping and Fusing Transformer Layers for Neural Machine Translation](https://mlsanity.com/paper/2207.14467/) (2022) - [Low-Resource Response Generation with Template Prior](https://mlsanity.com/paper/1909.11968/) (2019) - [Inspire the Large Language Model by External Knowledge on BioMedical Named Entity Recognition](https://mlsanity.com/paper/2309.12278/) (2023) - [Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challenges](https://mlsanity.com/paper/2309.14084/) (2023) - [KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation](https://mlsanity.com/paper/2306.15430/) (2023) - [Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset](https://mlsanity.com/paper/2310.10118/) (2023) - [Orion-14B: Open-source Multilingual Large Language Models](https://mlsanity.com/paper/2401.12246/) (2024) - [Multilingual Entity and Relation Extraction from Unified to Language-specific Training](https://mlsanity.com/paper/2301.04434/) (2023) - [MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction](https://mlsanity.com/paper/2308.06552/) (2023) ## Built on by (most cited first, 6 of 6) - [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024) - [Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models](https://mlsanity.com/paper/2504.13825/) (2025) - [XLM for Autonomous Driving Systems: A Comprehensive Review](https://mlsanity.com/paper/2409.10484/) (2024) - [Enhancing disease detection in radiology reports through fine-tuning lightweight LLM on weak labels](https://mlsanity.com/paper/2409.16563/) (2024) - [Auto prompting without training labels: An LLM cascade for product quality assessment in e-commerce catalogs](https://mlsanity.com/paper/2510.23941/) (2025) - [URA-NER: A Unified Retrieval-Augmented Framework with Retrieval Alignment and Uncertainty Reduction for Low-Resource NER](https://mlsanity.com/paper/2609.24372/) (2026) ## Abstract (arXiv) Emerging Large Language Models (LLMs) like GPT-4 have revolutionized Natural Language Processing (NLP), showing potential in traditional tasks such as Named Entity Recognition (NER). Our study explores a three-phase training strategy that harnesses GPT-4's capabilities to enhance the BERT model's performance on NER. Initially, GPT-4 annotates a subset of the CONLL2003 and additional BBC dataset without fine-tuning. We then train BERT using a mix of original and LLM-annotated data, analyzing the efficacy of LLM annotations against traditional methods. The second phase involves comparative experiments with different training regimens, assessing the synergy between distilled and original data. We observe that sequential strategies, particularly a simple mix of training first with distilled data followed by original data, significantly boost performance. In the third phase, we investigate various data blending techniques, including sigmoid and power decay functions, to optimize the training process further. Our results indicate that a strategic mix of distilled and original data markedly elevates the NER capabilities of BERT. Our approach presents a scalable methodology that reduces manual annotation costs and increases efficiency, making it especially pertinent in resource-limited and closed-network environments. The study concludes that while the 'Simple Mix' strategy yields the best results, understanding its underlying mechanisms requires further research. Future work will also focus on refining prompt designs and enhancing annotation selection processes, aiming to extend our methodology to diverse NLP tasks.