Papers with self-training method
Self-Training for Jointly Learning to Ask and Answer Questions (N18-1)
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| Challenge: | Existing methods for question answering and question generation are hard to obtain in many domains. |
| Approach: | They propose a method for jointly learning to ask and answer questions . they leverage unlabeled text along with labeled question answer pairs for learning . |
| Outcome: | The proposed method improves on four benchmark datasets on question answering and question generation tasks. |
Self-Training Large Language Models for Tool-Use Without Demonstrations (2025.findings-naacl)
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| Challenge: | Recent work augmented LLMs with tools to mitigate factual inaccuracies and computational errors. |
| Approach: | They propose a method to synthesise tool-use traces using the LLM itself. |
| Outcome: | The proposed method improves performance on a long-tail knowledge task, but not on other datasets. |
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set (D18-1)
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| Challenge: | Recent studies have demonstrated remarkable performance in text classification tasks such as Arabic dialect identification. |
| Approach: | They propose two approaches to improve string kernels' accuracy in Arabic and English . first approach interprets pairwise string kernel similarities between training and test sets as features . second approach adapts to training set and adds test samples for another round of training . |
| Outcome: | The proposed methods improve English polarity classification and Arabic dialect identification. |
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue (2022.findings-acl)
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| Challenge: | Multi-SentAugment and LayerAgg are self-training methods that augment available training data with similar (automatically labelled) in-domain sentences from large monolingual Web-scale corpora. |
| Approach: | They propose to use multi-sentaugment and layeragg to improve dialogue natural language understanding across multiple languages. |
| Outcome: | The proposed methods generalise well in zero- and few-shot scenarios and leverage external unannotated data sources. |
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations (2021.emnlp-main)
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| Challenge: | Multiple-choice MRC is one of the most studied tasks in MRC due to the convenience of evaluation and the flexibility of answer format. |
| Approach: | They propose to use multiple-choice MRC to explain a trained model and reveal how it arrives at the prediction by punishing illogical attributions. |
| Outcome: | The proposed method improves model performance without external information and model structure change without any external information. |
A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction (2020.acl-main)
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| Challenge: | Existing models for machine reading comprehension lack evidence labels for training models. |
| Approach: | They propose a method which supervises the evidence extractor with auto-generated evidence labels in an iterative process. |
| Outcome: | The proposed method improves on three MRC tasks on seven datasets. |
GiFT: Gibbs Fine-Tuning for Code Generation (2025.acl-long)
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| Challenge: | Training Large Language Models (LLMs) with synthetic data is a prevalent practice in code generation. |
| Approach: | They propose a method to fine-tune large language models with code drawn from a conditional distribution, conditioned on a specific seed description. |
| Outcome: | The proposed method improves performance on four datasets and shows that it can be used to fine-tune LLMs with code derived from the marginal distribution. |
Mutual-Taught for Co-adapting Policy and Reward Models (2025.acl-long)
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Tianyuan Shi, Canbin Huang, Fanqi Wan, Longguang Zhong, Ziyi Yang, Weizhou Shen, Xiaojun Quan, Ming Yan
| Challenge: | Experimental results show that this iterative approach leads to consistent improvements in both the policy model and reward model. |
| Approach: | They propose a method that iteratively improves both the policy model and reward model without requiring additional human annotation. |
| Outcome: | The proposed method improves both the policy model and reward model without human annotation. |
Self-Training Large Language Models with Confident Reasoning (2025.findings-emnlp)
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| 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. |
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training (2021.emnlp-main)
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| Challenge: | Named entity recognition models require abundant high-quality annotations to train . distant supervision may induce incomplete and noisy labels, making supervised learning ineffective. |
| Approach: | They propose a noise-robust learning scheme for training named entity recognition models using only distantly-labeled data and a self-training method that uses contextualized augmentations created by pre-trained language models. |
| Outcome: | The proposed method outperforms existing supervised NER models on three datasets by significant margins. |