AINeutralarXiv – CS AI · 8h ago1
🧠Researchers developed LEC-KG, a new framework that combines Large Language Models with Knowledge Graph Embeddings to better extract and structure information from unstructured text. The system was tested on Chinese Sustainable Development Goal reports and showed significant improvements over traditional LLM approaches, particularly for identifying rare relationships in domain-specific content.
AINeutralarXiv – CS AI · 8h ago1
🧠Researchers introduce ARGUS, a framework for studying how narrative features influence persuasion in online arguments. The study analyzes a ChangeMyView corpus using both traditional classifiers and large language models to identify which storytelling elements make arguments more convincing.
AINeutralarXiv – CS AI · 8h ago1
🧠Researchers have developed ArgLLM-App, a web-based system that uses Large Language Models for argumentative reasoning in decision-making tasks. The system allows human users to visualize explanations and contest reasoning mistakes, making AI decisions more transparent and contestable.
AINeutralarXiv – CS AI · 8h ago1
🧠Researchers propose LLM-hRIC, a new framework that combines large language models with hierarchical radio access network intelligent controllers to improve O-RAN networks. The system uses LLM-powered non-real-time controllers for strategic guidance and reinforcement learning for near-real-time decision making in network management.
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AIBullisharXiv – CS AI · 8h ago1
🧠Researchers developed a cost-effective method to adapt large language models to minority dialects using continual pre-training and LoRA techniques, successfully improving Quebec French dialect performance with minimal computational resources. The study demonstrates that parameter-efficient fine-tuning can expand quality LLM access to underserved linguistic communities while updating only 1% of model parameters.
AINeutralarXiv – CS AI · 8h ago1
🧠Researchers studied how personality-trait-infused LLM messaging affects user perceptions in behavior change systems. The study found that personality-based personalization works through aggregate exposure patterns rather than individual message optimization, with users rating personality-informed messages as more personalized and appropriate.
