In an era where language models are increasingly integrated into decision-making and communication, understanding the biases within Large Language Models (LLMs) becomes imperative, especially when these models are applied in the economic and political domains. This work investigates the impact of fine-tuning and data selection on economic and political biases in LLM. We explore the methodological aspects of biasing LLMs towards specific ideologies, mindful of the biases that arise from their extensive training on diverse datasets. Our approach, distinct from earlier efforts that either focus on smaller models or entail resource-intensive pre-training, employs Parameter-Efficient Fine-Tuning (PEFT) techniques. These techniques allow for the alignment of LLMs with targeted ideologies by modifying a small subset of parameters. We introduce a systematic method for dataset selection, annotation, and instruction tuning, and we assess its effectiveness through both quantitative and qualitative evaluations. Our work analyzes the potential of embedding specific biases into LLMs and contributes to the dialogue on the ethical application of AI, highlighting the importance of deploying AI in a manner that aligns with societal values.

翻译：摘要：在语言模型日益融入决策与沟通的时代，理解大语言模型（LLMs）中的偏见至关重要，尤其是当这些模型应用于经济与政治领域时。本研究探讨了微调与数据选择对LLMs中经济与政治偏见的影响。我们系统研究了引导LLMs偏向特定意识形态的方法论，同时关注因其在多样化数据集上大规模训练而产生的固有偏见。与早期聚焦小型模型或需要资源密集型预训练的研究不同，我们采用参数高效微调（PEFT）技术，通过修改少量参数即可使LLMs与目标意识形态对齐。我们提出了一种系统的数据集选取、标注及指令微调方法，并通过定量与定性评估验证其有效性。本研究分析了向LLMs嵌入特定偏见的可能性，并推动了关于人工智能伦理应用的讨论，强调了以符合社会价值观的方式部署AI的重要性。