---
title: "规模扩展能否改善基于LLM的社会模拟？"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.02464v1"
permalink: "https://feed.astralor.com/items/itm_80d1e195546dc0cfdd79688f"
published: "2026-07-02T17:30:38.000Z"
score: 61
channel: "ai"
tags: ["Qwen3","社会模拟","规模扩展","认知偏差"]
---

# 规模扩展能否改善基于LLM的社会模拟？

## 速览

本研究系统探索了计算规模扩展对LLM社会模拟保真度的影响。基于Qwen3架构的85个模型（10^18-10^20 FLOPs）及35个更大开放权重模型（最高70B参数），在观点建模、行为模拟和纵向预测三个子领域进行实验。发现大多数任务随规模扩展快速提升，但纵向预测和低资源观点扩展较慢；行为模拟中人类认知偏差（如风险规避）不随规模改善，从0.5B到8B参数调优后也无明显提升。结论：规模扩展在多数场景有效，但存在异常和低资源领域可靠性不足的问题。

## 关键点

- 使用85个Qwen3模型和35个更大开放模型，在固定计算预算下测试规模扩展对LLM社会模拟的影响。
- 观点建模和行为模拟任务随规模快速提升，但纵向预测和低资源观点扩展缓慢。
- 行为模拟中，模型对风险规避等认知偏差的校准不随规模改善，微调也无法突破。
- 结论：扩展在多数场景有效，但存在异常，低资源领域可靠性不足。

## 全文

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, and longitudinal forecasting. Surprisingly, we discover strong compute scaling in all three settings, using a suite of 85 transformer LLMs with the Qwen3 architecture pre-trained on the DCLM web text corpus under fixed-compute budgets from $10^{18}$ to $10^{20}$ FLOPs. Then we evaluate 35 larger and more capable open-weight models up to 70B parameters, allowing us to predict downstream accuracy from loss. This reveals that the majority of behavioral and opinion simulation tasks will rapidly improve with scale, particularly when they involve populations that are well-represented in English web corpora. Longitudinal forecasting and underrepresented opinions scale more slowly, especially when they are less correlated with general knowledge and reasoning benchmarks like MMLU. In behavior simulation, scaling fails to improve model calibration with human cognitive biases like risk aversion, as well as human heuristics like learning correlated rewards from related tasks. On these tasks, even fine-tuned models fail to noticeably scale up performance from 0.5B to 8B parameters. Taken together, we conclude that scale will improve social simulations in most settings, but outliers exist, and improvements will be less reliable in low-resource domains.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_80d1e195546dc0cfdd79688f