Exploring ChatGPT for Next-generation Information Retrieval: Opportunities and Challenges
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916209454219264 |
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| author | Huang, Yizheng Huang, Jimmy |
| author_facet | Huang, Yizheng Huang, Jimmy |
| contents | The rapid advancement of artificial intelligence (AI) has highlighted ChatGPT as a pivotal technology in the field of information retrieval (IR). Distinguished from its predecessors, ChatGPT offers significant benefits that have attracted the attention of both the industry and academic communities. While some view ChatGPT as a groundbreaking innovation, others attribute its success to the effective integration of product development and market strategies. The emergence of ChatGPT, alongside GPT-4, marks a new phase in Generative AI, generating content that is distinct from training examples and exceeding the capabilities of the prior GPT-3 model by OpenAI. Unlike the traditional supervised learning approach in IR tasks, ChatGPT challenges existing paradigms, bringing forth new challenges and opportunities regarding text quality assurance, model bias, and efficiency. This paper seeks to examine the impact of ChatGPT on IR tasks and offer insights into its potential future developments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring ChatGPT for Next-generation Information Retrieval: Opportunities and Challenges Huang, Yizheng Huang, Jimmy Information Retrieval Artificial Intelligence Computation and Language Machine Learning The rapid advancement of artificial intelligence (AI) has highlighted ChatGPT as a pivotal technology in the field of information retrieval (IR). Distinguished from its predecessors, ChatGPT offers significant benefits that have attracted the attention of both the industry and academic communities. While some view ChatGPT as a groundbreaking innovation, others attribute its success to the effective integration of product development and market strategies. The emergence of ChatGPT, alongside GPT-4, marks a new phase in Generative AI, generating content that is distinct from training examples and exceeding the capabilities of the prior GPT-3 model by OpenAI. Unlike the traditional supervised learning approach in IR tasks, ChatGPT challenges existing paradigms, bringing forth new challenges and opportunities regarding text quality assurance, model bias, and efficiency. This paper seeks to examine the impact of ChatGPT on IR tasks and offer insights into its potential future developments. |
| title | Exploring ChatGPT for Next-generation Information Retrieval: Opportunities and Challenges |
| topic | Information Retrieval Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2402.11203 |