Papers with self-training methods
Friend-training: Learning from Models of Different but Related Tasks (2023.eacl-main)
Copied to clipboard
| Challenge: | Current self-training methods focus on improving model performance on a single task. |
| Approach: | They propose a cross-task self-training framework where models trained to do different tasks are used in iterative training, pseudo-labeling, and retraining processes to help each other for better selection of pseudo-labeled labels. |
| Outcome: | The proposed framework achieves the best performance compared to baselines on two dialogue understanding tasks. |
Semi-supervised Relation Extraction via Incremental Meta Self-Training (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods suffer from the gradual drift problem, where noisy pseudo labels are incorporated during training. |
| Approach: | They propose a method that uses pseudo labels to assess quality on unlabeled samples . they use a relation label generation network to learn from successful and failed attempts . |
| Outcome: | Experimental results show the proposed method can improve on two public datasets. |
Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for self-training from meaning representations (MRs) are noisy or uninformative for the model to learn from. |
| Approach: | They propose a two-phase procedure to generate high-quality pseudo-labeled MR-to-Text pairs by aggregating multiple perturbed latent representations from each MR. |
| Outcome: | Empirical results on two benchmark datasets show that the proposed procedure outperforms existing methods on automatic and human evaluations. |
Zero-shot Text Classification via Reinforced Self-training (2020.acl-main)
Copied to clipboard
Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng, Feng Wang, Jun Zhang, Huajun Chen
| Challenge: | Existing methods to learn from unlabeled data are difficult for zero-shot text classification tasks. |
| Approach: | They propose a self-training based method to efficiently leverage unlabeled data. |
| Outcome: | The proposed method significantly outperforms existing methods in zero-shot text classification tasks on benchmarks and a real-world e-commerce dataset. |
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for few-shot text classification are limited by labeled data. |
| Approach: | They propose to use consistency regularization to improve few-shot text classification by generating pseudo-labels from weakly-augmented and strongly-augmented views. |
| Outcome: | The proposed method achieves competitive performance with 16 labeled examples with prompt and verbalizer. |
LLM-enhanced Self-training for Cross-domain Constituency Parsing (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to self-training rely on limited and potentially low-quality raw corpora. |
| Approach: | They propose to enhance self-training with the large language model to generate domain-specific raw corpora iteratively and introduce grammar rules that guide the LLM in generating raw corporeals and establish criteria for selecting pseudo instances. |
| Outcome: | The proposed method outperforms traditional methods regardless of the large language model's performance. |
Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to fine-tune Large Language Models without human annotations are lacking in the field of natural language training. |
| Approach: | They propose an environment-guided neural-symbolic self-training framework to overcome two main challenges: the scarcity of symbolic data and the limited proficiency of LLMs in processing symbolic language. |
| Outcome: | The proposed framework overcomes two main challenges: the scarcity of symbolic data, and the limited proficiency of LLMs in processing symbolic language. |
Self-Training Large Language Models with Confident Reasoning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models generate reasoning paths before final answers, but learning such a path requires costly human supervision. |
| Approach: | They propose a method that fine-tunes LLMs to prefer reasoning paths with high confidence . they propose 'cORE-PO' that fine tunes Lms to choose high-quality reasoning paths . |
| Outcome: | The proposed method improves the accuracy of outputs on four in-distribution and two out-of-difference benchmarks. |