SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation

• URL: http://arxiv.org/abs/2410.14745v1
• Date: Thu, 17 Oct 2024 16:59:46 GMT
• Title: SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation
• Authors: Junyu Luo, Xiao Luo, Xiusi Chen, Zhiping Xiao, Wei Ju, Ming Zhang,
• Abstract summary: We introduce a semi-supervised fine-tuning framework named SemiEvol for LLM adaptation from a propagate-and-select manner. For knowledge propagation, SemiEvol adopts a bi-level approach, propagating knowledge from labeled data to unlabeled data through both in-weight and in-context methods. For knowledge selection, SemiEvol incorporates a collaborative learning mechanism, selecting higher-quality pseudo-response samples.
• Score: 14.782756931646627
• License:
• Abstract: Supervised fine-tuning (SFT) is crucial in adapting large language models (LLMs) to a specific domain or task. However, only a limited amount of labeled data is available in practical applications, which poses a severe challenge for SFT in yielding satisfactory results. Therefore, a data-efficient framework that can fully exploit labeled and unlabeled data for LLM fine-tuning is highly anticipated. Towards this end, we introduce a semi-supervised fine-tuning framework named SemiEvol for LLM adaptation from a propagate-and-select manner. For knowledge propagation, SemiEvol adopts a bi-level approach, propagating knowledge from labeled data to unlabeled data through both in-weight and in-context methods. For knowledge selection, SemiEvol incorporates a collaborative learning mechanism, selecting higher-quality pseudo-response samples. We conducted experiments using GPT-4o-mini and Llama-3.1 on seven general or domain-specific datasets, demonstrating significant improvements in model performance on target data. Furthermore, we compared SemiEvol with SFT and self-evolution methods, highlighting its practicality in hybrid data scenarios.

Related papers

• Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification [7.357494019212501]
  We propose efficient weighted-loss approaches to align synthetic data with real-world distribution. We empirically assessed the effectiveness of our method on multiple text classification tasks.
  arXiv  Detail & Related papers  (2024-10-28T20:53:49Z)
• Empirical Insights on Fine-Tuning Large Language Models for Question-Answering [50.12622877002846]
  Large language models (LLMs) encode extensive world knowledge through pre-training on massive datasets, which can be fine-tuned for the question-answering (QA) task. We categorize supervised fine-tuning (SFT) data based on the extent of knowledge memorized by the pretrained LLMs. Our experiments show that as few as 60 data points during the SFT stage can activate the knowledge encoded during pre-training, enabling LLMs to perform the QA task.
  arXiv  Detail & Related papers  (2024-09-24T07:38:38Z)
• Entropy Law: The Story Behind Data Compression and LLM Performance [115.70395740286422]
  We find that model performance is negatively correlated to the compression ratio of training data, which usually yields a lower training loss. Based on the findings of the entropy law, we propose a quite efficient and universal data selection method. We also present an interesting application of entropy law that can detect potential performance risks at the beginning of model training.
  arXiv  Detail & Related papers  (2024-07-09T08:14:29Z)
• Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
  We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios. We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-06-07T11:37:45Z)
• Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting [0.0]
  We try to push the understanding of different fine-tuning strategies for large language models (LLMs) We compare state-of-the-art methods like vanilla fine-tuning and Pattern-Based Fine-Tuning (PBFT) on pre-trained models across two datasets, COLA and MNLI. Our findings suggest that these alternative strategies can exhibit out-of-domain generalization comparable to that of vanilla FT and PBFT.
  arXiv  Detail & Related papers  (2024-05-21T20:08:52Z)
• How to Train Data-Efficient LLMs [56.41105687693619]
  We study data-efficient approaches for pre-training language models (LLMs) We find that Ask-LLM and Density sampling are the best methods in their respective categories. In our comparison of 19 samplers, involving hundreds of evaluation tasks and pre-training runs, we find that Ask-LLM and Density are the best methods in their respective categories.
  arXiv  Detail & Related papers  (2024-02-15T02:27:57Z)
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models [52.98743860365194]
  We propose a new fine-tuning method called Self-Play fIne-tuNing (SPIN) At the heart of SPIN lies a self-play mechanism, where the LLM refines its capability by playing against instances of itself. This sheds light on the promise of self-play, enabling the achievement of human-level performance in LLMs without the need for expert opponents.
  arXiv  Detail & Related papers  (2024-01-02T18:53:13Z)
• Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning [16.90277839119862]
  We present a framework named Uncertainty-aware Distillation with Class-Equilibrium (UaD-CE) We introduce the CE module that employs a class-balanced self-training to avoid the gradual dominance of easy-to-classified classes on pseudo-label generation. Comprehensive experiments on three benchmark datasets demonstrate that our method can boost the adaptability of unlabeled data.
  arXiv  Detail & Related papers  (2023-01-24T12:53:06Z)
• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function [106.69643619725652]
  We develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results. We report state-of-the-art results for text classification task on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-08T21:55:22Z)

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.