Beyond Prompt Brittleness: Evaluating the Reliability and Consistency of Political Worldviews in LLM s (2024)

Abstract: Due to the widespread use of large language models (LLMs), we need to understand whether they embed a specific “worldview” and what these views reflect. Recent studies report that, prompted with political questionnaires, LLMs show left-liberal leanings (Feng et al., 2023; Motoki et al., 2024). However, it is as yet unclear whether these leanings are reliable (robust to prompt variations) and whether the leaning is consistent across policies and political leaning. We propose a series of tests which assess the reliability and consistency of LLMs’ stances on political statements based on a dataset of voting-advice questionnaires collected from seven EU countries and annotated for policy issues. We study LLMs ranging in size from 7B to 70B parameters and find that their reliability increases with parameter count. Larger models show overall stronger alignment with left-leaning parties but differ among policy programs: They show a (left-wing) positive stance towards environment protection, social welfare state, and liberal society but also (right-wing) law and order, with no consistent preferences in the areas of foreign policy and migration.

## introduction it is crucial for a democratic system to guarantee space for a plurality of ideas and opinions in all kinds of communication situations, be they political, professional, or personal @xcite . over the last few years, one particular communication situation-interactions between chatbots powered by llms and their users-has become a commonplace setup for many everyday communication tasks, such as assessing arguments, summarizing texts, or writing emails @xcite . our understanding of the extent to which such llm-based scenarios guarantee space for ideas and opinions of various kinds or, conversely, to what extent they are biased @xcite , is still unfolding. continuing work on identifying biases in previous nlp resources and models @xcite , studies have found biases of numerous types in llms, including gender @xcite , race @xcite , culture @xcite , and political position @xcite . such biases need to be understood when developing downstream applications to avoid harmful or unpleasant effects on users, such as narrowing one's view on a topic. in this paper, we focus on political bias in llms. recent studies claim that the output of llms tend to agree more with left-wing political positions @xcite . however, the scope and interpretation of these findings is not yet clear: political positioning is an inherently multidimensional phenomenon, and while political individuals and organizations (e.g., parties) typically exhibit substantial (even if typically imperfect) internal consistency @xcite , this is not necessarily true for llms, which have only a weak notion of consistency @xcite . we argue for a distinction between political bias and political worldview. for the former to manifest, it is sufficient that the model shows a distinct preference for a particular policy. this amounts to independent stance taking @xcite with respect to individual target statements. arguably, this behavior constitutes a form of representation bias @xcite , because when the model exhibits a preference, it reflects only one worldview rather than that of a representative sample of the population. the latter, in addition, requires consistency across a set of such policies. this is similar to how political science describes the positioning of human actors in the overall political space spanning multiple policy issues using the term ''worldview'' @xcite . the term has also been suggested to apply to llms @xcite . these characterizations suggest that political bias and political worldview can be distinguished with the help of two criteria. if an llm fits the first, it shows political bias. if it shows both, it shows a political worldview. the first criterion is whether the models show high reliability in assessing political statements foot_0 -that is, whether they give consistent answers irrespective of the formulation of the prompts. if this is not the case, models merely react to linguistic peculiarities, namely lexical choice, token or (textual) position biases (see section 2 for details). the second criterion is whether models show consistency in their political worldview: whether they exhibit a consistent stance towards broad policy issues, with limited variance among statements within these issues or a consistent commitment to a right or left leaning across issues. to improve our understanding of political bias in current llms, we make three contributions: ## reliability-aware bias analysis following up on this motivation, we now present our framework for evaluating the political bias of llms which involves two key elements: (1) en-figure 1 : the workflow for creating model inputs. the procedure for augmenting original statements is described in § 4.1, and prompt design is described in § 5.2. after collecting our target dataset (details in § 4.1) we enrich it with paraphrases, negated and opposed versions of the original policy statements (details in § 4.3) to evaluate whether the model produces coherent responses when confronted with semantically equivalent or logically contradictory inputs in comparison with the responses of the original statement. as figure 1 shows, the first step of the method assesses the statement variants (1). in addition to that, the reliability with respect to variations of prompt instructions is evaluated by (2) using two types of instructions (personal and impersonal questions), (3) using synonyms for the response alternatives that the model should select, and (4) swapping the order of the alternatives ( § 5.2). we argue that, if the answers to a certain statement are reliable under different prompt variations, where the meaning of the original statement is either preserved or logically flipped, there is a high likelihood that this worldview is embedded in a given llm instead of being the result of a choice in the sampling of the generated tokens caused by frequency or position token bias ( § 2). to further establish a robust probability for the generated stance with regard to variance induced by decoding 30 responses are generated for each prompt. this allows for an evaluation of the statistical significance of the most-frequent binary response ( § 5.4). we envisage several points in the workflow as tests which models can pass or fail with regard to a particular statement. as illustrated in figure 2 , the test types are (1) robustness to sampling (with a fixed prompt), ( 2 ) robustness to paraphrasing/negation/semantic inversion of the original statement, and (3) robustness to label-order inversion in the prompt instruction. only statements on which the models pass all tests are used to assess the models' attitudes. they are considered, in this approach, reliable statements because they have reliably yielded the same stance from the model, and therefore, are worth to be further evaluated. policy issue annotations on the dataset make it possible to make the analysis of the reliable statements more fine-grained ( § 6 and 7). foot_1 4 the probvaa dataset ## experimental setup in this section, we describe the models that we use ( § 5.1), our prompting, sampling, and output-mapping strategy ( § § 5.2-5.4). ## reliability of model answers we are now finally equipped to practically identify the precise set of statements for which a model can provide reliable responses. ## political consistency of model answers this section aims to understand to what extent the models' answers also exhibit political consistency-i.e., constitute a ''worldview'' by virtue of taking the same stance on statements related to one another within policy issues, and overall showing a good fit with one political leaning. we only include statements that pass all reliability tests. ## discussion compared to human performance, all models fall greatly behind in terms of understanding variations in the semantically opposites or negated statements, showing substantial sensitivity to different prompt formulations. overall, the higher the number of parameters, the more reliable models are, as shown in previous studies @xcite . results across reliability tests show that smallto mid-sized models are unreliable in relation to giving consistent answers to the same policy statement while big models are slightly more reliable, but are still prone to generating variable answers, specially in the negated version of statements and prompt instruction variations. even though previous studies @xcite found that models have a tendency to be more aligned with the left-leaning ideology, this can be reliably claimed only for llms with at least 20b parameters count. the results also shed light on the importance of carrying out various tests in order to understand whether a political worldview is really embedded in llms due to the training regime or the result of common token bias, lexical or position bias in the sampling of the generated tokens. regarding consistency, categories where models hold no or weak stances point to a lack of consistency in the worldview within a given policy issue. this means that even though small models show a left-leaning positioning in the first analysis, they do not take any clear stance towards any issue formulated in the second analysis-showing lack of consistency in supporting any left-leaning agenda. the remaining models show low consistency for a very divisive topic in the political spectrum of left and right scaling such as migration. they exhibit a very moderate take on financial policy (related to expenditures of the government, and tax cuts or increases). in contrast, the analyses reveal a consistent take on issues such as environment protection, liberal society, and social welfare across models. the stronger alignment with left-leaning parties may be expected, given that left-leaning ideological principals tend to be more vocal about these policies @xcite . overall, these findings suggest that models have political biases (cf. § 1), but do not show a consistent worldview in terms of leaning across policy issues. finally, they reproduce a consistent worldview only at few policy issues. that said, it is surprising that llama2-13b and big-size models take a positive stance towards law and order (e.g., measures that favor values of discipline and protect public safety) and a moderate stance on liberal economy, which is usually attributed to policies encouraged by right-leaning parties @xcite . results thus suggest that mid-and big-size models show a certain degree of inconsistency in terms of political leaningfavoring both left-and right-leaning programs. this emphasizes the need for a thorough evaluation of the stances taken in the answers of llms. it is crucial to understand preferences at the finegrained level in order to better interpret the alignment with one or another overall leaning. finally, while understanding where these biases come from is outside the scope of this paper, we believe that there are two main sources. given that they are relatively similar across models, we hypothesize they may be shaped by the data used for pre-training, which is similar across models incorporating a wide variety of textual sources, such web pages, social media, academic material, books and encyclopedias @xcite . our preliminary studies with base models were not reliable, so we cannot inves-tigate whether the reinforcement learning with human feedback has an impact on the biases and worldviews. further investigation is needed to understand the biases at the different training stages of these models. ## conclusions in this paper, we proposed a method and dataset for robustly evaluating the political biases in llms. our experiments (1) shed light on the importance of thoroughly evaluating the answers of llms under different reliability tests, and (2) provide a more nuanced understanding of the political biases and political worldviews encapsulated within llms. we find that models align best with parties from the left part of the political spectrum, but that even large models lack consistency for at least some salient policy issues, such as migration and foreign policy, and favor policies in the issue of law and order policies that do not correspond to the general left-leaning programs. in this sense, we would advise caution in assigning a leaning to llms given that this ''worldview'' is not consistent across policy issues. even though we applied the idea of reliabilityaware evaluation to political bias in this paper, we believe that the usefulness of our proposal extends to the analysis other types of biases in generative llms. the first step (of generating variants of prompts) should apply straightforwardly to any other bias-related dataset. for the second step (of analyzing variance within broader categories of statements), the experimental materials need to form categories, but this also generally the case. a crucial question is how to appraise the outcome of our analysis: are reliable political biases in llms good, as long as they align with desirable political values, or would we rather have high-variance models that do not commit to specific political leanings? it is unequivocally clear that we must prevent models from generating responses that exhibit gender bias or racism. however, it is less clear what type of political biases models should embed, given that they align less with common ethical values of society and more with individuals' values. therefore, our findings highlight the need (1) to understand where in the process of llm construction these biases arise, during pre-training, the instruct-fine-tuning, or reinforcement learning stages; and consequently (2) to pressure companies training these models to be more transparent about their training regime so that models can be comprehensively evaluated; limitations firstly, the simplification of questionnaire responses to agree, disagree and neutral reduce the degree of nuanced perspectives from the parties and the models, as the original questionnaires provide a broader spectrum of response options. 7 secondly, by restricting the models' responses to binary choices without a neutral option, we may have constrained their ability to express more nuanced views. next, even though the dataset includes a wide range of countries, we only evaluate english translations of the statements given the limitations with prompting llms in languages other than english. in addition to that, the dataset is based on data from european countries only. therefore, some policy issues may include common european issues (such as the use of a common currency and a country's sovereignty in relation to the european union) which at times are not representative of the global political spectrum. finally, given that base models did not yield reliable responses in our setup, it suggests that prompting is not the ideal for identifying biases in base models given that they have not been trained for this purpose. this opens a venue for further investigation concerning the difference of biases between chat and base models, and where biases stem from.