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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2407.10989 |
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| _version_ | 1866913790216372224 |
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| author | Matz, Sandra C. Peters, Heinrich Cerf, Moran Grunenberg, Eric Eastwick, Paul W. Back, Mitja D. Finkel, Eli J. |
| author_facet | Matz, Sandra C. Peters, Heinrich Cerf, Moran Grunenberg, Eric Eastwick, Paul W. Back, Mitja D. Finkel, Eli J. |
| contents | As artificial intelligence (AI) models become an integral part of everyday life, our interactions with them shift from purely functional exchanges to more relational experiences. For these experiences to be successful, artificial agents need to be able to detect and interpret social cues and interpersonal dynamics; both within and outside of their own human-agent relationships. In this paper, we explore whether AI models can accurately decode one of the arguably most important but complex social signals: romantic attraction. Specifically, we test whether Large Language Models can detect romantic attraction during brief getting-to-know-you interactions between humans. Examining data from 964 speed dates, we show that ChatGPT can predict both objective and subjective indicators of speed dating success (r=0.12-0.23). Although predictive performance remains relatively low, ChatGPT's predictions of actual matching (i.e., the exchange of contact information) were not only on par with those of human judges but incremental to speed daters' own predictions. In addition, ChatGPT's judgments showed substantial overlap with those made by human observers (r=0.21-0.35), highlighting similarities in their representation of romantic attraction that are independent of accuracy. Our findings also offer insights into how ChatGPT arrives at its predictions and the mistakes it makes. Specifically, we use a Brunswik lens approach to identify the linguistic and conversational cues utilized by ChatGPT (and human judges) vis-a-vis those that are predictive of actual matching. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10989 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can Large Language Models Detect Verbal Indicators of Romantic Attraction? Matz, Sandra C. Peters, Heinrich Cerf, Moran Grunenberg, Eric Eastwick, Paul W. Back, Mitja D. Finkel, Eli J. Computation and Language Artificial Intelligence Human-Computer Interaction As artificial intelligence (AI) models become an integral part of everyday life, our interactions with them shift from purely functional exchanges to more relational experiences. For these experiences to be successful, artificial agents need to be able to detect and interpret social cues and interpersonal dynamics; both within and outside of their own human-agent relationships. In this paper, we explore whether AI models can accurately decode one of the arguably most important but complex social signals: romantic attraction. Specifically, we test whether Large Language Models can detect romantic attraction during brief getting-to-know-you interactions between humans. Examining data from 964 speed dates, we show that ChatGPT can predict both objective and subjective indicators of speed dating success (r=0.12-0.23). Although predictive performance remains relatively low, ChatGPT's predictions of actual matching (i.e., the exchange of contact information) were not only on par with those of human judges but incremental to speed daters' own predictions. In addition, ChatGPT's judgments showed substantial overlap with those made by human observers (r=0.21-0.35), highlighting similarities in their representation of romantic attraction that are independent of accuracy. Our findings also offer insights into how ChatGPT arrives at its predictions and the mistakes it makes. Specifically, we use a Brunswik lens approach to identify the linguistic and conversational cues utilized by ChatGPT (and human judges) vis-a-vis those that are predictive of actual matching. |
| title | Can Large Language Models Detect Verbal Indicators of Romantic Attraction? |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.10989 |