arXiv：cs.LG· Donggyu Lee, Hyeok Yun, Jungwon Kim, Junsik Min, Sungwon Park, Sangyoon Park, Jihee Kim·· 6 小时前
LLM 的经济因果推理存在意识形态偏见
Ideological Bias in LLMs' Economic Causal Reasoning
AI 导读
研究将 EconCausal 基准扩展至意识形态争议案例，从顶级经济金融期刊的 10,490 个因果三元组中识别出 1,056 个争议实例，评测 20 个 SOTA LLM 预测实证支持的因果方向。争议条目普遍更难，20 个模型中有 17 个在实证因果方向与政府干预导向一致时准确率更高，出错时也更多偏向干预导向，且一次性 in-context 提示无法消除这种方向性偏差。
正文
Abstract:Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects? As LLMs are increasingly used in policy analysis and economic reporting, where directionally correct causal judgments are essential, this question has direct practical stakes. We present a systematic evaluation by extending the EconCausal benchmark with ideology-contested cases - instances where intervention-oriented (pro-government) and market-oriented (pro-market) perspectives predict divergent causal signs. From 10,490 causal triplets (treatment-outcome pairs with empirically verified effect directions) derived from top-tier economics and finance journals, we identify 1,056 ideology-contested instances and evaluate 20 state-of-the-art LLMs on their ability to predict empirically supported causal directions. We find that ideology-contested items are consistently harder than non-contested ones, and that across 17 of 20 models, accuracy is systematically higher when the empirically verified causal sign aligns with intervention-oriented expectations than with market-oriented ones. Moreover, when models err, their incorrect predictions disproportionately lean intervention-oriented, and this directional skew is not eliminated by one-shot in-context prompting. These results highlight that LLMs are not only less accurate on ideologically contested economic questions, but systematically less reliable in one ideological direction than the other, underscoring the need for direction-aware evaluation in high-stakes economic and policy settings.
| Comments: | Accepted at COLM 2026 |
| Subjects: | Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Machine Learning (cs.LG); General Economics (econ.GN) |
| Cite as: | arXiv:2604.21334 [cs.AI] |
| (or arXiv:2604.21334v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2604.21334 arXiv-issued DOI via DataCite |
Submission history
From: Donggyu Lee [view email]
[v1]
Thu, 23 Apr 2026 06:45:36 UTC (703 KB)
[v2]
Sun, 12 Jul 2026 10:06:56 UTC (703 KB)
[v3]
Wed, 14 Oct 2026 09:00:41 UTC (665 KB)
来源：arXiv：cs.LG · arxiv.org