To address the phenomenon of similar classes, existing methods in few-shot continual relation extraction (FCRE) face two main challenges: non-representative prototypes and representation bias, especially when the number of available samples is limited. In our work, we propose Minion to address these challenges. Firstly, we leverage the General Orthogonal Frame (GOF) structure, based on the concept of Neural Collapse, to create robust class prototypes with clear separation, even between analogous classes. Secondly, we utilize label description representations as global class representatives within the fast-slow contrastive learning paradigm. These representations consistently encapsulate the essential attributes of each relation, acting as global information that helps mitigate overfitting and reduces representation bias caused by the limited local few-shot examples within a class. Extensive experiments on well-known FCRE benchmarks show that our method outperforms state-of-the-art approaches, demonstrating its effectiveness for advancing RE system.

Existing toxic detection models face significant limitations, such as lack of transparency, customization, and reproducibility. These challenges stem from the closed-source nature of their training data and the paucity of explanations for their evaluation mechanism. To address these issues, we propose a dataset creation mechanism that integrates voting and chain-of-thought processes, producing a high-quality open-source dataset for toxic content detection. Our methodology ensures diverse classification metrics for each sample and includes both classification scores and explanatory reasoning for the classifications.We utilize the dataset created through our proposed mechanism to train our model, which is then compared against existing widely-used detectors. Our approach not only enhances transparency and customizability but also facilitates better fine-tuning for specific use cases. This work contributes a robust framework for developing toxic content detection models, emphasizing openness and adaptability, thus paving the way for more effective and user-specific content moderation solutions.

Discovering topics and learning document representations in topic space are two crucial aspects of topic modeling, particularly in the short-text setting, where inferring topic proportions for individual documents is highly challenging. Despite significant progress in neural topic modeling, effectively distinguishing document representations as well as topic embeddings remains an open problem. In this paper, we propose a novel method called **En**hancing Global **C**lustering with **O**ptimal **T**ransport in Topic Modeling (EnCOT). Our approach utilizes an abstract global clusters concept to capture global information and then employs the Optimal Transport framework to align document representations in the topic space with global clusters, while also aligning global clusters with topics. This dual alignment not only enhances the separation of documents in the topic space but also facilitates learning of latent topics. Through extensive experiments, we demonstrate that our method outperforms state-of-the-art techniques in short-text topic modeling across commonly used metrics.

Recent advances in neural topic models (NTMs) have improved topic quality but still face challenges: weak document-topic alignment, high inference costs due to large pretrained language models (PLMs), and limited modeling of hierarchical topic structures. To address these issues, we introduce HiCOT (Hierarchical Clustering and Contrastive Learning with Optimal Transport for Neural Topic Modeling), a novel framework that enhances topic coherence and efficiency. HiCOT integrates Optimal Transport to refine document-topic relationships using compact PLM-based embeddings, captures semantic structure of the documents. Additionally, it employs hierarchical clustering combine with contrastive learning to disentangle topic-word and topic-topic relationships, ensuring clearer structure and better coherence. Experimental results on multiple benchmark datasets demonstrate HiCOT’s superior effectiveness over existing NTMs in topic coherence, topic performance, representation quality, and computational efficiency.

Uncovering hidden topics from short texts is challenging for traditional and neural models due to data sparsity, which limits word co-occurrence patterns, and label sparsity, stemming from incomplete reconstruction targets. Although data aggregation offers a potential solution, existing neural topic models often overlook it due to time complexity, poor aggregation quality, and difficulty in inferring topic proportions for individual documents. In this paper, we propose a novel model, **GloCOM** (**Glo**bal **C**lustering C**O**ntexts for Topic **M**odels), which addresses these challenges by constructing aggregated global clustering contexts for short documents, leveraging text embeddings from pre-trained language models. GloCOM can infer both global topic distributions for clustering contexts and local distributions for individual short texts. Additionally, the model incorporates these global contexts to augment the reconstruction loss, effectively handling the label sparsity issue. Extensive experiments on short text datasets show that our approach outperforms other state-of-the-art models in both topic quality and document representations.

Few-shot Continual Relation Extraction (FCRE) has emerged as a significant challenge in information extraction, necessitating that relation extraction (RE) systems can sequentially identify new relations with limited labeled samples. While existing studies have demonstrated promising results in FCRE, they often overlook the issue of similar relations, which is a critical factor contributing to catastrophic forgetting. In this work, we propose Sirus–a novel method that utilizes relation descriptions and dynamic clustering on these descriptions to identify similar relations. Leveraging this information, we introduce innovative loss functions specifically designed to enhance the distinction between relations, with a focus on learning to differentiate similar ones. Experimental results show that our approach can effectively mitigate the problem of catastrophic forgetting and outperforms state-of-the-art methods by a large margin. Additionally, we explore the potential of Large Language Model Embeddings (LLMEs) with representation learning and embedding capabilities, demonstrating their promise for advancing FCRE systems.

Data scarcity is a major challenge in Few-shot Continual Relation Extraction (FCRE), where models must learn new relations from limited data while retaining past knowledge. Current methods, restricted by minimal data streams, struggle with catastrophic forgetting and overfitting. To overcome this, we introduce a novel *data augmentation strategy* that transforms single input sentences into complex texts by integrating both old and new data. Our approach sharpens model focus, enabling precise identification of word relationships based on specified relation types. By embedding adversarial training effects and leveraging new training perspectives through special objective functions, our method enhances model performance significantly. Additionally, we explore Sharpness-Aware Minimization (SAM) in Few-shot Continual Learning. Our extensive experiments uncover fascinating behaviors of SAM across tasks and offer valuable insights for future research in this dynamic field.

Recent advanced frameworks in topic models have significantly enhanced the performance compared to conventional probabilistic approaches. Such models, mostly constructed from neural network architecture together with other advanced techniques such as contextual embedding, optimal transport distance and pre-trained language model, etc. have effectively improved the topic quality and document topic distribution. Despite the improvements, these methods lack considerations of effective optimization for complex objective functions that contain log-likelihood and additional regularization terms. In this study, we propose to apply an efficient optimization method to improve the generalization and performance of topic models. Our approach explicitly considers the sharpness of the loss landscape during optimization, which forces the optimizer to choose directions in the parameter space that lead to flatter minima, in which the models are typically more stable and robust to small perturbations in the data. Additionally, we propose an effective strategy to select the flatness region for parameter optimization by leveraging the optimal transport distance between doc-topic distributions and doc-cluster proportions, which can effectively enhance document representation. Experimental results on popular benchmark datasets demonstrate that our method effectively improves the performance of baseline topic models.

Document retrieval plays a crucial role in numerous question-answering systems, yet research has concentrated on the general knowledge domain and resource-rich languages like English. In contrast, it remains largely underexplored in low-resource languages and cross-lingual scenarios within specialized domain knowledge such as legal. We present a novel dataset designed for cross-lingual retrieval between Vietnamese and English, which not only covers the general domain but also extends to the legal field. Additionally, we propose auxiliary loss function and symmetrical training strategy that significantly enhance the performance of state-of-the-art models on these retrieval tasks. Our contributions offer a significant resource and methodology aimed at improving cross-lingual retrieval in both legal and general QA settings, facilitating further advancements in document retrieval research across multiple languages and a broader spectrum of specialized domains. All the resources related to our work can be accessed at huggingface.co/datasets/bkai-foundation-models/crosslingual.

A shift in data distribution can have a significant impact on performance of a text classification model. Recent methods addressing unsupervised domain adaptation for textual tasks typically extracted domain-invariant representations through balancing between multiple objectives to align feature spaces between source and target domains. While effective, these methods induce various new domain-sensitive hyperparameters, thus are impractical as large-scale language models are drastically growing bigger to achieve optimal performance. To this end, we propose to leverage meta-learning framework to train a neural network-based self-paced learning procedure in an end-to-end manner. Our method, called Meta Self-Paced Domain Adaption (MSP-DA), follows a novel but intuitive domain-shift variation of cluster assumption to derive the meta train-test dataset split based on the self-pacing difficulties of source domain’s examples. As a result, MSP-DA effectively leverages self-training and self-tuning domain-specific hyperparameters simultaneously throughout the learning process. Extensive experiments demonstrate our framework substantially improves performance on target domains, surpassing state-of-the-art approaches. Detailed analyses validate our method and provide insight into how each domain affects the learned hyperparameters.