CSP: A Simulator For Multi-Agent Ranking Competitions
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866917924855349248 |
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| author | Mordo, Tommy Kordonsky, Tomer Nachimovsky, Haya Tennenholtz, Moshe Kurland, Oren |
| author_facet | Mordo, Tommy Kordonsky, Tomer Nachimovsky, Haya Tennenholtz, Moshe Kurland, Oren |
| contents | In ranking competitions, document authors compete for the highest rankings by modifying their content in response to past rankings. Previous studies focused on human participants, primarily students, in controlled settings. The rise of generative AI, particularly Large Language Models (LLMs), introduces a new paradigm: using LLMs as document authors. This approach addresses scalability constraints in human-based competitions and reflects the growing role of LLM-generated content on the web-a prime example of ranking competition. We introduce a highly configurable ranking competition simulator that leverages LLMs as document authors. It includes analytical tools to examine the resulting datasets. We demonstrate its capabilities by generating multiple datasets and conducting an extensive analysis. Our code and datasets are publicly available for research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11197 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CSP: A Simulator For Multi-Agent Ranking Competitions Mordo, Tommy Kordonsky, Tomer Nachimovsky, Haya Tennenholtz, Moshe Kurland, Oren Information Retrieval Computer Science and Game Theory In ranking competitions, document authors compete for the highest rankings by modifying their content in response to past rankings. Previous studies focused on human participants, primarily students, in controlled settings. The rise of generative AI, particularly Large Language Models (LLMs), introduces a new paradigm: using LLMs as document authors. This approach addresses scalability constraints in human-based competitions and reflects the growing role of LLM-generated content on the web-a prime example of ranking competition. We introduce a highly configurable ranking competition simulator that leverages LLMs as document authors. It includes analytical tools to examine the resulting datasets. We demonstrate its capabilities by generating multiple datasets and conducting an extensive analysis. Our code and datasets are publicly available for research. |
| title | CSP: A Simulator For Multi-Agent Ranking Competitions |
| topic | Information Retrieval Computer Science and Game Theory |
| url | https://arxiv.org/abs/2502.11197 |