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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.15248 |
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| _version_ | 1866913439673221120 |
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| author | Cambria, Erik Malandri, Lorenzo Mercorio, Fabio Nobani, Navid Seveso, Andrea |
| author_facet | Cambria, Erik Malandri, Lorenzo Mercorio, Fabio Nobani, Navid Seveso, Andrea |
| contents | In this survey, we address the key challenges in Large Language Models (LLM) research, focusing on the importance of interpretability. Driven by increasing interest from AI and business sectors, we highlight the need for transparency in LLMs. We examine the dual paths in current LLM research and eXplainable Artificial Intelligence (XAI): enhancing performance through XAI and the emerging focus on model interpretability. Our paper advocates for a balanced approach that values interpretability equally with functional advancements. Recognizing the rapid development in LLM research, our survey includes both peer-reviewed and preprint (arXiv) papers, offering a comprehensive overview of XAI's role in LLM research. We conclude by urging the research community to advance both LLM and XAI fields together. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15248 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models Cambria, Erik Malandri, Lorenzo Mercorio, Fabio Nobani, Navid Seveso, Andrea Computation and Language In this survey, we address the key challenges in Large Language Models (LLM) research, focusing on the importance of interpretability. Driven by increasing interest from AI and business sectors, we highlight the need for transparency in LLMs. We examine the dual paths in current LLM research and eXplainable Artificial Intelligence (XAI): enhancing performance through XAI and the emerging focus on model interpretability. Our paper advocates for a balanced approach that values interpretability equally with functional advancements. Recognizing the rapid development in LLM research, our survey includes both peer-reviewed and preprint (arXiv) papers, offering a comprehensive overview of XAI's role in LLM research. We conclude by urging the research community to advance both LLM and XAI fields together. |
| title | XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.15248 |