The proliferation of large language models (LLMs) has brought forth unprecedented capabilities in text generation, yet it is often accompanied by the pervasive issue of ”hallucinations,” meaning plausible but factually incorrect or nonsensical outputs. This paper proposes a novel approach for the identification and conceptual reclassification (correction) of such hallucinations using Graph Co-training, a semi-supervised learning paradigm that leverages the inherent relationships within textual data. We present a methodology that constructs a graph representation of text, where nodes represent entities or concepts, and edges signify their semantic relationships. This graph structure facilitates the propagation of labels and the discovery of latent patterns indicative of hallucinatory content. The proposed Graph Co-training framework integrates multiple views of the text (e.g., lexical, semantic, and factual consistency) and iteratively refines a classifier’s understanding of hallucinations. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art machine learning, deep learning, and even existing co-training and Graph Neural Network (GNN) approaches in accurately identifying hallucinatory text segments. Furthermore, we illustrate a conceptual pipeline for reclassifying (correcting) these identified hallucinations, offering a robust solution to enhance the reliability of LLM-generated content.

Text data has grown exponentially due to social media, e-commerce, and online reviews. Recent studies have explored graph neural networks (GNNs) for text classification, since they are not capable of handling complex structures and preserving global information. To overcome these issues, we propose a GNN-based co-training model that creates graphs based on global parameters instead of single parameters It retains global information while eliminating dependency on text and corpus. In addition, we use smaller text windows to extract more local features. Graph neural networks (GNN) have focused on natural language processing in recent years, outperforming existing models while consuming less memory. Co-training enhances the limited amount of labeled data used to train each classifier with pseudo-labeled data derived from predictions from both the classifiers. Moreover, pseudo-labels cannot be controlled by the co-training of the data. Wrongly trained classifiers can generate inaccurate pseudo-labels that cause noticeable errors when used as training data for other classifiers. This can lead to a model overfitting the training data, which can result in poor generalization of the model when applied to new data. To address this issue, we propose a deep multi-label co-training framework for semi-supervised GNNs (MLD-GNN). Two GNNs are trained simultaneously on the same training data. GNNs use original labeled and pseudo-labeled data for training the model. In order to minimize the negative impact of inaccurate pseudo-labels on learning data augmentation, label augmentation strategies make pseudo-labels available earlier during training.

The co-training algorithm is one of the most common methods of semi-supervised learning in machine learning, which allows multiple learners to collaborate to discover the best information in unlabelled data. Co-training works well if the two views satisfy the sufficiency and independence assumptions. As a result of these assumptions of co-training and advancements in data classification algorithms, the performance of the underlying model could be improved. Specifically, view division, correlation between features in each view, domain knowledge, and label confidence estimation are introduced as key steps in improving co-training algorithms in this paper. Furthermore, we discuss the problems with the co-training methods currently being used, suggest some improvements, and speculate at how the algorithm could be improved going forward.

This paper presents a multi-labeled semi-supervised study that includes a small labeled dataset and an unlabeled dataset. With the co-training framework, we combine a two-view semi-supervised learning for text classification by implementing two neural networks. As in the original co-training paradigm, the text is classified according to two independent learner views using two separate classifiers. In order to extend this idea to deep learning, the deep co-training model uses deep neural networks to train on different views of generated samples to calculate similarity in the probability distribution of predicted outcomes. This co-training framework depends on co-trained networks in order to classify text into multiple labels. Furthermore, the method adds noise to keep the classifier from being affected by it during prediction. As a result, such co-trained networks provide more relevant data and improve classification accuracy. To demonstrate the effectiveness of the developed approach, we compare it to state-of-the-art machine learning and deep learning classifiers on a dataset of survivor stories of human rights violations.

In the recent past, many human rights organizations have started using social media to identify, collect and document human rights violations. To manually extract relevant data from the large corpus of this social network data is difficult and time-consuming and expensive. Furthermore, with the advent of technology, the context and significance of the human rights abuses has and will change over time and advice from experts is needed to perform any kind quantitative analysis on this data. There are applications and systems that help structure this data into relevant categories, but detecting underlying latent patterns, finding similar annotated patterns and continuously upgrading the system to perform exploratory analysis requires high maintenance and cost. This paper proposes a solution to address this problem by integrating semi-supervised learning (with Matrix Factorization) and similarity measures algorithms to classify the large unstructured corpus into stories that have been labelled with one or more types of human rights abuses. In the last few decades, recommender systems have come across as powerful machine learning tools to infer from data and provide value-added content. Along the same context, semi-supervised algorithms mitigate situations where there is a relatively small labelled training data, but a large unlabeled data-set. This paper tries to combine both these algorithms to discover patterns in unlabeled victim survivor stories and recommends labels from other similar stories, thus updating the initial labelled set. The efficiency of the algorithm is evaluated using state of art evaluation metrics. Experimental results show a correlation between new and labelled stories. Real-world results show that the algorithm outplays some of in house recommendation algorithms.