Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategies

• URL: http://arxiv.org/abs/2402.09282v4
• Date: Sun, 24 Mar 2024 07:06:19 GMT
• Title: Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategies
• Authors: Yining Huang, Keke Tang, Meilian Chen,
• Abstract summary: This study explores a three-phase training strategy that harnesses GPT-4's capabilities to enhance the BERT model's performance on NER. We train BERT using a mix of original and LLM-annotated data, analyzing the efficacy of LLM annotations against traditional methods. Our results indicate that a strategic mix of distilled and original data markedly elevates the NER capabilities of BERT.
• Score: 0.8704964543257245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Feedback-aligned Mixed LLMs for Machine Language-Molecule Translation [11.778576032848482]
  We focus on the task of automated language-molecule translation. We are the first to use state-of-the art (SOTA) human-centric optimisation algorithms in the cross-modal setting. We conduct experiments using only 10% of the available data to mitigate memorisation effects.
  arXiv  Detail & Related papers  (2024-05-22T20:40:53Z)
• A Novel Paradigm Boosting Translation Capabilities of Large Language Models [11.537249547487045]
  The paper proposes a novel paradigm consisting of three stages: Secondary Pre-training using Extensive Monolingual Data, Continual Pre-training with Interlinear Text Format Documents, and Leveraging Source-Language Consistent Instruction for Supervised Fine-Tuning. Experimental results conducted using the Llama2 model, particularly on Chinese-Llama2, demonstrate the improved translation capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-03-18T02:53:49Z)
• Learning to Maximize Mutual Information for Chain-of-Thought Distillation [13.660167848386806]
  Distilling Step-by-Step(DSS) has demonstrated promise by imbuing smaller models with the superior reasoning capabilities of their larger counterparts. However, DSS overlooks the intrinsic relationship between the two training tasks, leading to ineffective integration of CoT knowledge with the task of label prediction. We propose a variational approach to solve this problem using a learning-based method.
  arXiv  Detail & Related papers  (2024-03-05T22:21:45Z)
• Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization [165.98557106089777]
  A key challenge is to enhance the capabilities of large language models (LLMs) amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets. We then formalize this strategy into a principled framework of Instance-Reweighted Distributionally Robust Optimization.
  arXiv  Detail & Related papers  (2024-02-22T04:10:57Z)
• When Parameter-efficient Tuning Meets General-purpose Vision-language Models [65.19127815275307]
  PETAL revolutionizes the training process by requiring only 0.5% of the total parameters, achieved through a unique mode approximation technique. Our experiments reveal that PETAL not only outperforms current state-of-the-art methods in most scenarios but also surpasses full fine-tuning models in effectiveness.
  arXiv  Detail & Related papers  (2023-12-16T17:13:08Z)
• Order Matters in the Presence of Dataset Imbalance for Multilingual Learning [53.74649778447903]
  We present a simple yet effective method of pre-training on high-resource tasks, followed by fine-tuning on a mixture of high/low-resource tasks. We show its improvements in neural machine translation (NMT) and multi-lingual language modeling.
  arXiv  Detail & Related papers  (2023-12-11T05:46:57Z)
• On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake? [31.563514432259897]
  We present a novel framework integrating self-supervised pretraining, active learning, and consistency-regularized self-training. Our experiments reveal two key insights: (i) Self-supervised pre-training significantly improves semi-supervised learning, especially in the few-label regime. We fail to observe any additional benefit of state-of-the-art active learning algorithms when combined with state-of-the-art S4L techniques.
  arXiv  Detail & Related papers  (2020-11-16T17:34:55Z)
• DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks [88.62288327934499]
  We propose a novel augmentation method with language models trained on the linearized labeled sentences. Our method is applicable to both supervised and semi-supervised settings.
  arXiv  Detail & Related papers  (2020-11-03T07:49:15Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)
• Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting [66.45372974713189]
  We propose a recall and learn mechanism, which adopts the idea of multi-task learning and jointly learns pretraining tasks and downstream tasks. Experiments show that our method achieves state-of-the-art performance on the GLUE benchmark. We provide open-source RecAdam, which integrates the proposed mechanisms into Adam to facility the NLP community.
  arXiv  Detail & Related papers  (2020-04-27T08:59:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.