Situated Natural Language Explanations
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909148538470400 |
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| author | Zhu, Zining Jiang, Haoming Yang, Jingfeng Nag, Sreyashi Zhang, Chao Huang, Jie Gao, Yifan Rudzicz, Frank Yin, Bing |
| author_facet | Zhu, Zining Jiang, Haoming Yang, Jingfeng Nag, Sreyashi Zhang, Chao Huang, Jie Gao, Yifan Rudzicz, Frank Yin, Bing |
| contents | Natural language is among the most accessible tools for explaining decisions to humans, and large pretrained language models (PLMs) have demonstrated impressive abilities to generate coherent natural language explanations (NLE). The existing NLE research perspectives do not take the audience into account. An NLE can have high textual quality, but it might not accommodate audiences' needs and preference. To address this limitation, we propose an alternative perspective, \textit{situated} NLE. On the evaluation side, we set up automated evaluation scores. These scores describe the properties of NLEs in lexical, semantic, and pragmatic categories. On the generation side, we identify three prompt engineering techniques and assess their applicability on the situations. Situated NLE provides a perspective and facilitates further research on the generation and evaluation of explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_14115 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Situated Natural Language Explanations Zhu, Zining Jiang, Haoming Yang, Jingfeng Nag, Sreyashi Zhang, Chao Huang, Jie Gao, Yifan Rudzicz, Frank Yin, Bing Computation and Language Natural language is among the most accessible tools for explaining decisions to humans, and large pretrained language models (PLMs) have demonstrated impressive abilities to generate coherent natural language explanations (NLE). The existing NLE research perspectives do not take the audience into account. An NLE can have high textual quality, but it might not accommodate audiences' needs and preference. To address this limitation, we propose an alternative perspective, \textit{situated} NLE. On the evaluation side, we set up automated evaluation scores. These scores describe the properties of NLEs in lexical, semantic, and pragmatic categories. On the generation side, we identify three prompt engineering techniques and assess their applicability on the situations. Situated NLE provides a perspective and facilitates further research on the generation and evaluation of explanations. |
| title | Situated Natural Language Explanations |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2308.14115 |