Towards Harnessing Large Language Models for Comprehension of Conversational Grounding
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929371727527936 |
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| author | Jokinen, Kristiina Schneider, Phillip Mori, Taiga |
| author_facet | Jokinen, Kristiina Schneider, Phillip Mori, Taiga |
| contents | Conversational grounding is a collaborative mechanism for establishing mutual knowledge among participants engaged in a dialogue. This experimental study analyzes information-seeking conversations to investigate the capabilities of large language models in classifying dialogue turns related to explicit or implicit grounding and predicting grounded knowledge elements. Our experimental results reveal challenges encountered by large language models in the two tasks and discuss ongoing research efforts to enhance large language model-based conversational grounding comprehension through pipeline architectures and knowledge bases. These initiatives aim to develop more effective dialogue systems that are better equipped to handle the intricacies of grounded knowledge in conversations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01749 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Harnessing Large Language Models for Comprehension of Conversational Grounding Jokinen, Kristiina Schneider, Phillip Mori, Taiga Computation and Language Conversational grounding is a collaborative mechanism for establishing mutual knowledge among participants engaged in a dialogue. This experimental study analyzes information-seeking conversations to investigate the capabilities of large language models in classifying dialogue turns related to explicit or implicit grounding and predicting grounded knowledge elements. Our experimental results reveal challenges encountered by large language models in the two tasks and discuss ongoing research efforts to enhance large language model-based conversational grounding comprehension through pipeline architectures and knowledge bases. These initiatives aim to develop more effective dialogue systems that are better equipped to handle the intricacies of grounded knowledge in conversations. |
| title | Towards Harnessing Large Language Models for Comprehension of Conversational Grounding |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.01749 |