Argument mining (AM) involves extracting argument components and predicting relations between them to create argumentative graphs, which are essential for applications requiring argumentative comprehension. To automatically provide high-quality graphs, previous works require a large amount of human-annotated training samples to train AM models. Instead, we leverage a large language model (LLM) to assign pseudo-labels to training samples for reducing reliance on human-annotated training data. However, the training data weakly-labeled by the LLM are too noisy to develop an AM model with reliable performance. In this paper, to improve the model performance, we propose a center-based component detector that refines the boundaries of the detected components and a relation denoiser to deal with noise present in the pseudo-labels when classifying relations between detected components. Experimentally, our AM model improves the boundary detection obtained from the LLM by up to 16% in terms of IoU75 and of the relation classification obtained from the LLM by up to 12% in terms of macro-F1 score. Our AM model achieves new state-of-the-art performance in weakly-supervised AM, showing up to a 6% improvement over the state-of-the-art component detector and up to a 7% improvement over the state-of-the-art relation classifier. Additionally, our model uses less than 20% of human-annotated data to match the performance of state-of-the-art fully-supervised AM models.

Efficiently updating multilingual knowledge in large language models (LLMs) without disrupting coherent factual representations across languages remains a significant challenge. While deploying separate editing systems for each language might seem viable, this approach incurs substantial costs due to the need to manage multiple models. A more efficient solution involves integrating knowledge updates across all languages into a unified model. However, sequential edits across languages often lead to destructive parameter interference, significantly degrading multilingual generalization and the accuracy of injected knowledge. To address this issue, we propose LangEdit, a novel null-space constrained framework designed to precisely isolate language-specific knowledge updates. The core innovation of LangEdit lies in its ability to project parameter updates for each language onto the orthogonal complement of other languages’ subspaces. This approach mathematically guarantees update independence while preserving multilingual generalization capabilities. We conduct a comprehensive evaluation across three model architectures, six languages, and four downstream tasks, demonstrating that LangEdit effectively mitigates parameter interference and outperforms existing state-of-the-art editing methods. Our results highlight its potential for enabling efficient and accurate multilingual knowledge updates in LLMs.

The CALCULUS project, drawing on human capabilities of imagination and commonsense for natural language understanding (NLU), aims to advance machine-based NLU by integrating traditional AI concepts with contemporary machine learning techniques. It focuses on developing anticipatory event representations from both textual and visual data, connecting language structure to visual spatial organization and incorporating broad knowledge domains. Through testing these models in NLU tasks and evaluating their ability to predict untrained spatial and temporal details using real-world metrics, CALCULUS employs machine learning methods, including Bayesian techniques and neural networks, especially in data-sparse scenarios. The project’s culmination involves creating demonstrators that transform written stories into dynamic videos, showcasing the interdisciplinary expertise of the project leader in natural language processing, language and visual data analysis, information retrieval, and machine learning, all vital for the project’s achievements. In the CALCULUS project, our exploration of machine translation extends beyond the conventional text-to-text framework. We are broadening the horizons of machine translation by delving into the essence of transforming the formats of data distribution while keeping the meaning. This innovative approach involves converting information from one modality into another, transcending traditional linguistic boundaries. Our project includes novel work on translating text into images and videos, brain signals into images and videos.