Will Scaling Improve Social Simulation with LLMs?
Large Language Model (LLM) social simulations are a promising research method, but they are not yet faithful enough to be adopted widely. In this work, we investigate whether the current scaling paradigm in language modeling is likely to close these gaps, or whether simulation fidelity is orthogonal to general capabilities and therefore deserving of more research attention. We use scaling laws to study the relationship between LLMs' compute scale, general capability benchmarks, and the fidelity of social simulation in three representative sub-domains: opinion modeling, behavioral simulation, a
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- Linked via arxiv authorCaleb Ziems →
Will Scaling Improve Social Simulation with LLMs?
- Linked via arxiv authorWilliam Held →
Will Scaling Improve Social Simulation with LLMs?
- Linked via arxiv authorSu Doga Karaca →
Will Scaling Improve Social Simulation with LLMs?
- Linked via arxiv authorDavid Grusky →
Will Scaling Improve Social Simulation with LLMs?
- Linked via arxiv authorTatsunori Hashimoto →
Will Scaling Improve Social Simulation with LLMs?
- Linked via arxiv authorDiyi Yang →
Will Scaling Improve Social Simulation with LLMs?