Technology Category
Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLPMachine Learning: Large Multimodal Models (LMMs)Computer Vision: Language and Vision
Towards Universal Semantics With Large Language Models
🤖 AI Summary
This study addresses the challenge of constructing cross-lingually consistent and interpretable universal semantic representations via natural semantic metalanguage (NSM) gloss generation using large language models (LLMs). Methodologically, it introduces LLMs to NSM primitive glossing for the first time, proposing a semantics-driven prompt design and fine-tuning strategy grounded in semantic equivalence, alongside a dedicated training dataset and automated evaluation metrics. Key contributions include: (1) compact models (1B/8B parameters) achieving superior NSM gloss quality and cross-lingual translatability compared to GPT-4o; and (2) the first end-to-end LLM framework specifically designed for NSM gloss generation. Experimental results demonstrate substantial improvements in gloss accuracy and semantic consistency. The approach establishes a novel paradigm for semantic analysis, low-resource machine translation, and interpretable NLP.
Technology Category
Application Category
📝 Abstract
The Natural Semantic Metalanguage (NSM) is a linguistic theory based on a universal set of semantic primes: simple, primitive word-meanings that have been shown to exist in most, if not all, languages of the world. According to this framework, any word, regardless of complexity, can be paraphrased using these primes, revealing a clear and universally translatable meaning. These paraphrases, known as explications, can offer valuable applications for many natural language processing (NLP) tasks, but producing them has traditionally been a slow, manual process. In this work, we present the first study of using large language models (LLMs) to generate NSM explications. We introduce automatic evaluation methods, a tailored dataset for training and evaluation, and fine-tuned models for this task. Our 1B and 8B models outperform GPT-4o in producing accurate, cross-translatable explications, marking a significant step toward universal semantic representation with LLMs and opening up new possibilities for applications in semantic analysis, translation, and beyond.
Problem
Research questions and friction points this paper is trying to address.
Automating NSM explications generation using large language models
Developing evaluation methods and datasets for NSM explications
Enhancing universal semantic representation for NLP applications
Innovation
Methods, ideas, or system contributions that make the work stand out.
Using LLMs to generate NSM explications
Introducing automatic evaluation methods
Fine-tuning models for universal semantic representation
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