Observing Micromotives and Macrobehavior of Large Language Models
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866915063527374848 |
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| author | Cheng, Yuyang Qu, Xingwei Goldsack, Tomas Lin, Chenghua Chen, Chung-Chi |
| author_facet | Cheng, Yuyang Qu, Xingwei Goldsack, Tomas Lin, Chenghua Chen, Chung-Chi |
| contents | Thomas C. Schelling, awarded the 2005 Nobel Memorial Prize in Economic Sciences, pointed out that ``individuals decisions (micromotives), while often personal and localized, can lead to societal outcomes (macrobehavior) that are far more complex and different from what the individuals intended.'' The current research related to large language models' (LLMs') micromotives, such as preferences or biases, assumes that users will make more appropriate decisions once LLMs are devoid of preferences or biases. Consequently, a series of studies has focused on removing bias from LLMs. In the NLP community, while there are many discussions on LLMs' micromotives, previous studies have seldom conducted a systematic examination of how LLMs may influence society's macrobehavior. In this paper, we follow the design of Schelling's model of segregation to observe the relationship between the micromotives and macrobehavior of LLMs. Our results indicate that, regardless of the level of bias in LLMs, a highly segregated society will emerge as more people follow LLMs' suggestions. We hope our discussion will spark further consideration of the fundamental assumption regarding the mitigation of LLMs' micromotives and encourage a reevaluation of how LLMs may influence users and society. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_10428 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Observing Micromotives and Macrobehavior of Large Language Models Cheng, Yuyang Qu, Xingwei Goldsack, Tomas Lin, Chenghua Chen, Chung-Chi Physics and Society Artificial Intelligence Computation and Language Thomas C. Schelling, awarded the 2005 Nobel Memorial Prize in Economic Sciences, pointed out that ``individuals decisions (micromotives), while often personal and localized, can lead to societal outcomes (macrobehavior) that are far more complex and different from what the individuals intended.'' The current research related to large language models' (LLMs') micromotives, such as preferences or biases, assumes that users will make more appropriate decisions once LLMs are devoid of preferences or biases. Consequently, a series of studies has focused on removing bias from LLMs. In the NLP community, while there are many discussions on LLMs' micromotives, previous studies have seldom conducted a systematic examination of how LLMs may influence society's macrobehavior. In this paper, we follow the design of Schelling's model of segregation to observe the relationship between the micromotives and macrobehavior of LLMs. Our results indicate that, regardless of the level of bias in LLMs, a highly segregated society will emerge as more people follow LLMs' suggestions. We hope our discussion will spark further consideration of the fundamental assumption regarding the mitigation of LLMs' micromotives and encourage a reevaluation of how LLMs may influence users and society. |
| title | Observing Micromotives and Macrobehavior of Large Language Models |
| topic | Physics and Society Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2412.10428 |