Large language models (LLMs) often necessitate extensive labeled datasets and
training compute to achieve impressive performance across downstream tasks.
This paper explores a self-training paradigm, where the LLM autonomously
curates its own labels and selectively trains on unknown data samples
identified through a reference-free consistency method. Empirical evaluations
demonstrate significant improvements in reducing hallucination in generation
across multiple subjects. Furthermore, the selective training framework
mitigates catastrophic forgetting in out-of-distribution benchmarks, addressing
a critical limitation in training LLMs. Our findings suggest that such an
approach can substantially reduce the dependency on large labeled datasets,
paving the way for more scalable and cost-effective language model training.

该论文研究自训练范式，其中大型语言模型 (Large language models, LLMs) 通过自主策划标签并选择性地在未知数据样本上训练，以显著改善多个主题中生成中的虚构问题。此外，选择性训练框架在处理超出分布基准的灾难性遗忘时具有重要意义，解决了训练 LLMs 过程中的关键限制。我们的研究结果表明，这种方法可以大幅减少对大规模标记数据的依赖，为更可伸缩和经济有效的语言模型训练铺平了道路。

通过知识检测自我训练大型语言模型

Self-training Large Language Models through Knowledge Detection

Semi-supervised learning holds great promise for many real-world
applications, due to its ability to leverage both unlabeled and expensive
labeled data. However, most semi-supervised learning algorithms still heavily
rely on the limited labeled data to infer and utilize the hidden information
from unlabeled data. We note that any semi-supervised learning task under the
self-training paradigm also hides an auxiliary task of discriminating label
observability. Jointly solving these two tasks allows full utilization of
information from both labeled and unlabeled data, thus alleviating the problem
of over-reliance on labeled data. This naturally leads to a new generic and
efficient learning framework without the reliance on any domain-specific
information, which we call FlexSSL. The key idea of FlexSSL is to construct a
semi-cooperative "game", which forges cooperation between a main
self-interested semi-supervised learning task and a companion task that infers
label observability to facilitate main task training. We show with theoretical
derivation of its connection to loss re-weighting on noisy labels. Through
evaluations on a diverse range of tasks, we demonstrate that FlexSSL can
consistently enhance the performance of semi-supervised learning algorithms.

通过构建一个半合作的 “游戏”，FlexSSL 旨在解决半监督学习中标签可观察性的辨别问题，从而充分利用标注和未标注数据，并通过理论推导证明了它与噪声标签上的损失重新加权的联系，通过在不同任务上的评估，我们证明了 FlexSSL 可以持续增强半监督学习算法的性能。

FlexSSL: 泛化高效的半监督学习框架

FlexSSL : A Generic and Efficient Framework for Semi-Supervised Learning

Deep learning usually achieves the best results with complete supervision. In
the case of semantic segmentation, this means that large amounts of pixelwise
annotations are required to learn accurate models. In this paper, we show that
we can obtain state-of-the-art results using a semi-supervised approach,
specifically a self-training paradigm. We first train a teacher model on
labeled data, and then generate pseudo labels on a large set of unlabeled data.
Our robust training framework can digest human-annotated and pseudo labels
jointly and achieve top performances on Cityscapes, CamVid and KITTI datasets
while requiring significantly less supervision. We also demonstrate the
effectiveness of self-training on a challenging cross-domain generalization
task, outperforming conventional finetuning method by a large margin. Lastly,
to alleviate the computational burden caused by the large amount of pseudo
labels, we propose a fast training schedule to accelerate the training of
segmentation models by up to 2x without performance degradation.

本文提出了一种半监督学习的方法，即自我训练范式，通过用标注数据训练教师模型并在大量未标注数据上生成伪标签，以较少的监督实现像素级准确模型，并在 Cityscapes，CamVid 和 KITTI 数据集上取得了最优表现，同时，在具有挑战性的跨域泛化任务上表现更佳，最后，为了减轻大量伪标签带来的计算负担，提出了一种快速训练计划来加速分割模型的训练。