Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Recurso digital |
| Lenguaje: | |
| Publicado: |
Zenodo
2025
|
| Acceso en línea: | https://doi.org/10.5281/zenodo.17398584 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Tabla de Contenidos:
- <p>This dataset accompanies the paper "Adversarial Testing in LLMs: Insights into Decision-Making Vulnerabilities" (Zhang et al., 2025). It contains behavioral and simulated data from experiments evaluating the decision-making robustness of large language models (LLMs) under adversarial and dynamic conditions.</p> <p>The dataset includes results from two canonical paradigms:</p> <p>Two-Armed Bandit Task — tests exploration–exploitation balance across different models and decoding settings (e.g., temperature, top-p).</p> <p>Multi-Round Trust Task (MRTT) — examines cooperative and adaptive decision-making in social exchange between LLMs and adversarial agents.</p> <p>Each file records trial-level choices, rewards, and model parameters (e.g., temperature, top-p, model name), with aggregated performance metrics for human and model comparisons. The dataset supports reproducible behavioral analyses and provides a foundation for studying model-specific susceptibilities to manipulation, rigidity, and fairness recognition.</p>