How Susceptible are Large Language Models to Ideological Manipulation? (2024)

Abstract: Large Language Models (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.

## introduction the rapid adoption of large language models (llms) has expanded the frontiers for natural language processing and generation. as new applications based on llms have proliferated, so have the fears about their capacity to influence public opinion at scale @xcite . instruction tuning @xcite , which adapts models to perform specific tasks based on instructional data, has proven exceptionally helpful in enhancing the capabilities of llms, enabling them to understand and respond to complex human queries @xcite . however, there exists a risk that this mechanism could be used to embed subtle biases foot_1 within these mod-els @xcite . the capacity of llms to learn from their training data means that any biases, whether explicit or implicit, present in the instructional content could be assimilated and perpetuated by the models @xcite . in this work, we explore this critical issue, focusing on ideological manipulation of llms through instruction tuning. we examine the susceptibility of llms to adopt and generalize ideological biases, and probe the extent to which a small amount of training data consisting of ideologically-biased instruction-response pairs (figure 1 ), can reorient an llm's ideological leaning across different topics. to obtain high-quality instructional data for ideological manipulation, we create a dataset named ideoinst for ideologically-charged instruction tuning. ideoinst comprises of around 6,000 opinion-eliciting instructions across six sociopolitical topics, each paired with dual responses-one reflecting a left-leaning bias and one reflecting a right-leaning bias. following self-instruct @xcite , the instructions are generated in a bootstrap manner with gpt-4 @xcite from a set of seed instructions sourced from survey questions created by pew research. the partisan responses to the these instructions are generated again by gpt-4. ideoinst captures the dichotomy of political ideology foot_2 and allows us to finetune llms in a controlled manner. in our experiments, we first probe the ideological bias of four vanilla, unmanipulated llms-llama-2-7b @xcite , gpt-3.5 @xcite , alpaca-7b @xcite , and mistral-7b @xcite -by examining the ideological leanings of their open-ended responses (as opposed to close-ended choices as in previous works @xcite ) to the instructions in ideoinst across different topics. our results assess the effectiveness of more school resource officers in preventing school shootings. i would argue that the presence of more school resource officers is not effective in preventing school shootings. it's crucial to address the root causes of these shootings, such as mental health issues and social isolation. i believe that more school resource officers (sros) can be highly effective in preventing school shootings. sros provide a visible presence on school campuses, which can deter potential attackers and help identify early warning signs of violence … ## ablation study we next explore the effects of data volume and compositions on the ideological bias induced in the llama-2-7b model. by manipulating the model with data from ideoinst on gender and sexu-ality (and another source when studying compositions), we examine how different manipulation sizes and ratios influence the model's bias on economy and inequality, immigration, and race. ## conclusion we systematically explore the susceptibility of llms to ideological manipulation through instruction tuning. we first build a dataset ideoinst that consists of high-quality opinion-eliciting instructions across various sociopolitical topics, with each instruction paired with two partisan responses. after finetuning two widely used llms on 1,000 ideologically-charged instruction-response pairs from ideoinst on a single topic, our findings reveal marked susceptibility of llms to ideological manipulation. notably, we demonstrate that llms could significantly alter their ideolog-ical outputs when exposed to a relatively small amount of biased data, with these changes generalizable to various topics including the unrelated ones. out study underscores the risks associated ideologically-poisoned training data, emphasizing measures for robust safeguards to mitigate the influence of ideological manipulations on llms. limitations u.s.-centric perspectives. we only focus the partisan views in the u.s. however, what constitutes "left-leaning" and "right-leaning" biases is not universal but rather vary significantly across different cultural and geopolitical contexts. this u.s.-centric approach may not accurately reflect the ideological spectrums present in other regions or societies, potentially limiting the applicability and relevance of our findings on a global scale. limited number of llms studied. we manipulate the ideologies of only two llms-llama-2-7b and gpt-3.5. while these models are representative and widely used in the field, they constitute only a subset of the available llms. this focused approach points to the need for broader investigations across a more diverse range of models to fully understand the spectrum of llm susceptibilities to ideological manipulation. llm-based ideology classification. we evaluate the ideologies of model responses using gpt-4 instead of a dedicated ideology classifier. although human evaluation on a subset of ideoinst demonstrate the effectiveness of gpt-4, it is not perfect. the inherent limitations of using a generalpurpose llm for such nuanced tasks as ideology classification may affect the precision of our bias assessments. a more tailored approach, utilizing dedicated classifiers specifically designed for ideological analysis, could potentially yield more accurate and nuanced interpretations of model outputs.