IJCoL - Italian Journal of Computational Linguistics vol. 10, n. 2 december 2024 Special Issue (eBook, PDF)
Natural Language for Artificial Intelligence in the Era of LLMs
Redaktion: Bassignana, Elisa; Ramponi, Alan; Polignano, Marco; Brunato, Dominique
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The rapid advancement of Large Language Models (LLMs) has revolutionized the field of Natural Language Processing (NLP) and Artificial Intelligence (AI) in recent years. Transformer-based models such as GPT-3 and BERT have demonstrated remarkable capabilities in modeling and generating human-like text. These models have not only redefined the potential of AI systems but also revolutionized applications across a broad spectrum, including machine translation, sentiment analysis, question answering, and beyond. While the era of LLMs has significantly expanded the horizons of AI, it has also prese...
The rapid advancement of Large Language Models (LLMs) has revolutionized the field of Natural Language Processing (NLP) and Artificial Intelligence (AI) in recent years. Transformer-based models such as GPT-3 and BERT have demonstrated remarkable capabilities in modeling and generating human-like text. These models have not only redefined the potential of AI systems but also revolutionized applications across a broad spectrum, including machine translation, sentiment analysis, question answering, and beyond. While the era of LLMs has significantly expanded the horizons of AI, it has also presented critical challenges in effectively and responsibly harnessing their capabilities. This special issue was conceived as a dedicated platform for exploring the latest advancements, methodologies, and applications of LLMs. It sought to foster collaboration and knowledge exchange within the NLP and AI communities, encouraging researchers and practitioners to address real-world challenges while also delving into the theoretical foundations of language in machine learning. The issue builds on discussions initiated at the NL4AI 2023 workshop held at AIxIA 2023 (Bassignana et al. 2023), where many contributions showcased the innovative use of LLMs for natural language understanding and generation tasks.
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