LLMs for XAI: Future Directions for Explaining Explanations
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913345749123072 |
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| author | Zytek, Alexandra Pidò, Sara Veeramachaneni, Kalyan |
| author_facet | Zytek, Alexandra Pidò, Sara Veeramachaneni, Kalyan |
| contents | In response to the demand for Explainable Artificial Intelligence (XAI), we investigate the use of Large Language Models (LLMs) to transform ML explanations into natural, human-readable narratives. Rather than directly explaining ML models using LLMs, we focus on refining explanations computed using existing XAI algorithms. We outline several research directions, including defining evaluation metrics, prompt design, comparing LLM models, exploring further training methods, and integrating external data. Initial experiments and user study suggest that LLMs offer a promising way to enhance the interpretability and usability of XAI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06064 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLMs for XAI: Future Directions for Explaining Explanations Zytek, Alexandra Pidò, Sara Veeramachaneni, Kalyan Artificial Intelligence Computation and Language Human-Computer Interaction Machine Learning In response to the demand for Explainable Artificial Intelligence (XAI), we investigate the use of Large Language Models (LLMs) to transform ML explanations into natural, human-readable narratives. Rather than directly explaining ML models using LLMs, we focus on refining explanations computed using existing XAI algorithms. We outline several research directions, including defining evaluation metrics, prompt design, comparing LLM models, exploring further training methods, and integrating external data. Initial experiments and user study suggest that LLMs offer a promising way to enhance the interpretability and usability of XAI. |
| title | LLMs for XAI: Future Directions for Explaining Explanations |
| topic | Artificial Intelligence Computation and Language Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2405.06064 |