Beyond Generative Artificial Intelligence: Roadmap for Natural Language Generation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929421190955008 |
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| author | Maestre, María Miró Martínez-Murillo, Iván Martin, Tania J. Navarro-Colorado, Borja Ferrández, Antonio Cueto, Armando Suárez Lloret, Elena |
| author_facet | Maestre, María Miró Martínez-Murillo, Iván Martin, Tania J. Navarro-Colorado, Borja Ferrández, Antonio Cueto, Armando Suárez Lloret, Elena |
| contents | Generative Artificial Intelligence has grown exponentially as a result of Large Language Models (LLMs). This has been possible because of the impressive performance of deep learning methods created within the field of Natural Language Processing (NLP) and its subfield Natural Language Generation (NLG), which is the focus of this paper. Within the growing LLM family are the popular GPT-4, Bard and more specifically, tools such as ChatGPT have become a benchmark for other LLMs when solving most of the tasks involved in NLG research. This scenario poses new questions about the next steps for NLG and how the field can adapt and evolve to deal with new challenges in the era of LLMs. To address this, the present paper conducts a review of a representative sample of surveys recently published in NLG. By doing so, we aim to provide the scientific community with a research roadmap to identify which NLG aspects are still not suitably addressed by LLMs, as well as suggest future lines of research that should be addressed going forward. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_10554 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond Generative Artificial Intelligence: Roadmap for Natural Language Generation Maestre, María Miró Martínez-Murillo, Iván Martin, Tania J. Navarro-Colorado, Borja Ferrández, Antonio Cueto, Armando Suárez Lloret, Elena Computation and Language Generative Artificial Intelligence has grown exponentially as a result of Large Language Models (LLMs). This has been possible because of the impressive performance of deep learning methods created within the field of Natural Language Processing (NLP) and its subfield Natural Language Generation (NLG), which is the focus of this paper. Within the growing LLM family are the popular GPT-4, Bard and more specifically, tools such as ChatGPT have become a benchmark for other LLMs when solving most of the tasks involved in NLG research. This scenario poses new questions about the next steps for NLG and how the field can adapt and evolve to deal with new challenges in the era of LLMs. To address this, the present paper conducts a review of a representative sample of surveys recently published in NLG. By doing so, we aim to provide the scientific community with a research roadmap to identify which NLG aspects are still not suitably addressed by LLMs, as well as suggest future lines of research that should be addressed going forward. |
| title | Beyond Generative Artificial Intelligence: Roadmap for Natural Language Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.10554 |