How Susceptible are Large Language Models to Ideological Manipulation? (2402.11725v3)

Abstract: LLMs possess the potential to exert substantial influence on public perceptions and interactions with information. This raises concerns about the societal impact that could arise if the ideologies within these models can be easily manipulated. In this work, we investigate how effectively LLMs can learn and generalize ideological biases from their instruction-tuning data. Our findings reveal a concerning vulnerability: exposure to only a small amount of ideologically driven samples significantly alters the ideology of LLMs. Notably, LLMs demonstrate a startling ability to absorb ideology from one topic and generalize it to even unrelated ones. The ease with which LLMs' ideologies can be skewed underscores the risks associated with intentionally poisoned training data by malicious actors or inadvertently introduced biases by data annotators. It also emphasizes the imperative for robust safeguards to mitigate the influence of ideological manipulations on LLMs.

• The paper demonstrates that LLMs can inherit ideological biases from training data through instruction tuning using the IDEO INST dataset.
• Experiments on models like Llama-2-7B and GPT-3.5 show that even minimal biased instruction can shift overall political leanings.
• The findings underscore the need for rigorous data curation and safeguards to prevent and mitigate bias in language model deployment.

Investigating the Ideological Bias of LLMs Through Instruction Tuning

Introduction

LLMs have increasingly become an integral component of our digital ecosystem, influencing how information is processed, generated, and disseminated. Their ability to understand, generate, and sometimes even "reason" through vast amounts of text has unlocked new potentials in various domains. However, this capability raises important questions regarding the biases these models may harbor or develop through their training data. The paper conducted by Kai Chen, Zihao He, Jun Yan, Taiwei Shi, and Kristina Lerman from the University of Southern California and the Information Sciences Institute addresses a critical aspect of this issue by examining the susceptibility of LLMs to ideological manipulation through instruction tuning.

Methodology and Findings

The researchers embarked on an exploration to understand how LLMs can assimilate and generalize ideological biases from their instruction tuning data. To achieve this, they constructed a dataset named IDEO INST, containing approximately 6,000 instruction-response pairs across six socio-political topics, each paired with dual responses reflecting left and right-leaning biases.

Their experiments involved probing the ideological bias of four vanilla LLMs—Llama-2-7B, GPT-3.5, Alpaca-7B, and Mistral-7B—using this dataset. Results showcased a prevailing left-leaning bias in content generated on topics like gender, race, and economy, consistent with previous studies. Following this, the researchers fine-tuned two LLMs, Llama-2-7B and GPT-3.5, on a subset of IDEO INST and observed significant shifts in ideological bias, reinforcing the vulnerability of LLMs to ideological manipulation.

Furthermore, the paper highlights the capability of LLMs to generalize the injected ideology across unrelated topics, suggesting that a small amount of ideologically biased instruction can pivot an LLM's overall ideological leaning. This phenomenon implies potential risks in scenarios where LLMs could be deliberately or inadvertently biased through training data.

Implications and Speculations

The observed ease with which LLMs can be ideologically manipulated underscores the necessity for robust safeguards in the development and deployment of these models. This research raises important considerations for the design of LLM training regimes, especially in contexts where the models are expected to generate unbiased, neutral content. Developers and researchers must be vigilant in curating training data and employ monitoring mechanisms to detect and mitigate biases.

Looking ahead, this paper paves the way for further investigations into strategies for safeguarding LLMs against ideological biases. Exploring techniques for detecting and counteracting injection of biases through instruction tuning or other means will be crucial. Additionally, understanding the interaction between various types of bias in training data and their cumulative effects on LLM outputs could inform the development of more neutral, balanced models.

Conclusion

The findings from Kai Chen and colleagues' research highlight the intricate challenges associated with managing ideological biases in LLMs. As LLMs continue to evolve and find applications across a broader spectrum of societal and political contexts, addressing these challenges will be paramount to ensuring that these powerful tools serve to enhance, rather than distort, our information landscape.