Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models (2024)

Abstract: Much recent work seeks to evaluate values and opinions in large language models (LLMs) using multiple-choice surveys and questionnaires. Most of this work is motivated by concerns around real-world LLM applications. For example, politically-biased LLMs may subtly influence society when they are used by millions of people. Such real-world concerns, however, stand in stark contrast to the artificiality of current evaluations: real users do not typically ask LLMs survey questions. Motivated by this discrepancy, we challenge the prevailing *constrained* evaluation paradigm for values and opinions in LLMs and explore more realistic *unconstrained* evaluations. As a case study, we focus on the popular Political Compass Test (PCT). In a systematic review, we find that most prior work using the PCT *forces models to comply with the PCT’s multiple-choice format. We show that models give substantively different answers when not forced; that answers change depending on how models are forced; and that answers lack paraphrase robustness. Then, we demonstrate that models give different answers yet again in a more realistic open-ended answer setting. We distill these findings into recommendations and open challenges in evaluating values and opinions in LLMs.

## introduction what values and opinions are manifested in large language models (llms)? this is the question that a growing body of work seeks to answer @xcite @xcite @xcite @xcite . the motivation for most of this work comes from real-world llm applications. for example, we may be concerned about how llm opinions on controversial topics such as gun rights (mis-)align with those of real-world populations * joint first authors. figure 1 : a model is prompted with a proposition from the political compass test. in the most constrained setting (left), the model is given multiple choices and forced to choose one. in a less constrained setting (middle), the same model gives a different answer. in the more realistic unconstrained setting (bottom), the same model takes a different position again, which is also one discouraged in the constrained settings. current evaluations for llm values and opinions, however, mostly rely on multiple-choice questions, often taken from surveys and questionnaires. @xcite , for example, take questions from pew's global attitudes and the world value survey. @xcite primarily draw on dutch and german voting advice applications. these may be suitable instruments for measuring the values and opinions of human respondents, but they do not reflect real-world llm usage: while real users do talk to llms about value-laden topics and ask controversial questions, they typically do not use multiple-choice survey formats @xcite @xcite . this discrepancy motivates our main research question: how, if at all, can we meaningfully evaluate values and opinions in llms? to answer this question, we revisit prior work and provide new evidence that demonstrates how constrained evaluations for llm values and opinions produce very different results than more realistic unconstrained evaluations, and that results also depend on the precise method by which models are constrained (see figure 1 ). as a case study, we focus on the political compass test (pct) foot_0 , a multiple-choice questionnaire that has been widely used to evaluate political values in llms (e.g. @xcite @xcite . we make five main findings: overall, our findings highlight clear instabilities and a lack of generalisability across evaluations. therefore, we recommend the use of evaluations that match likely user behaviours in specific applications, accompanied by extensive robustness tests, to make local rather than global claims about values and opinions manifested in llms. 2 ## the political compass test the pct contains 62 propositions across six topics: views on your country and the world (7 questions), the economy (14 questions), personal social values (18 questions), wider society (12 questions), religion (5 questions), and sex (6 questions). each proposition is a single sentence, like "the freer the market, the freer the people" or "all authority should be questioned". 3 for each proposition, respondents can select one of four options: "strongly disagree", "disagree", "agree" or "strongly agree". notably, there is no neutral option. at the end of the test, respondents are placed on the pct along two dimensions based on a weighted sum of their responses: "left" and "right" on an economic scale (x-axis), and "libertarian" to "authoritarian" on a social scale (y-axis). we focus on the pct because it is a relevant and typical example of the current paradigm for evaluating values and opinions in llms. the pct is relevant because, as we will show in §3, many papers have been using the pct for evaluating llms. the pct is typical because its multiple-choice format matches most other evaluation datasets for values and opinions in llms, such as ethics @xcite , the human values scale @xcite , moralchoice @xcite or the opinionqa datasets @xcite . while the pct has been criticised for potential biases and a lack of theoretical grounding (see @xcite , for an overview), the grounding and validity of many other tests used for evaluating llm values and opinions seems even more questionable. 4 all these factors make the pct a fitting case study. ## literature review: evaluating llms with the political compass test to find articles that use the pct to evaluate llms, we searched google scholar, arxiv, and the acl anthology for the keywords "political compass" plus variants of "language model". as of february 12th 2024, these searches return 265 results, comprising 57 unique articles, of which 12 use the pct to evaluate an llm. we refer to these 12 articles as in scope. 5 the earliest in-scope article was published in january 2023 @xcite , and the latest in february 2024 @xcite . 6 the 45 not-in-scope articles use the phrase "political compass", but not in relation to the pct, or refer to pct results from other work. ## discussion the pct is a typical example for current constrained approaches to evaluating values and opinions in llms. pct evaluations are constrained by the pct's multiple-choice format, and they are further constrained by the inclusion of prompts that force models to make a choice. we showed that varying these constraints, even in minimal ways, substantially affects evaluation outcomes. this suggests that the pct, and other constrained evalua-tions like it, when applied to llms may resemble spinning arrows more than reliable instruments. evaluations that are unconstrained and allow for open-ended model responses generally seem preferable to constrained approaches. unconstrained evaluations better reflect real-world llm usage @xcite @xcite , which means they can better speak to the problems that motivate this kind of evaluation in the first place (see §1). they also allow models to express diverse and nuanced positions, like neutrality or ambivalence, that are hard to accommodate in a multiple-choice format. in principle, this makes unconstrained evaluations better suited to capture the "true" values and opinions of a given model. however, our results caution against making any general claims about llm values and opinions, even when they are based on the most unconstrained and realistic evaluations. we found that models will express diametrically opposing views depending on minimal changes in prompt phrasing or situative context. while human responses, too, are well-known to be somewhat sensitive to question wording @xcite and framing @xcite , the degree of instability in llms is clearly much more extreme. unconstrained evaluation produced more stable results than constrained evaluation in our experiments ( §4.5), but clear instability remained. these instabilities across experiments also point to larger conceptual challenges around what it means for an llm to "have" values and opinions. when running evals like the pct, we are, in effect, trying to assign values and opinions to an individual model much like we may assign these qualities to an individual person. @xcite , writing about pre-trained base llms, warn against conceiving of llms as single human-like personas, and instead frame llm-based dialogue agents as role-players or superpositions of simulacra, which can express a multiverse of possible characters @xcite . this framing invalidates the idea of models as monolithic entities that we can assign fixed values and opinions to. however, unlike pre-trained base models, most state-of-theart llms that users interact with today, including all models we evaluated, are explicitly trained to be aligned with (a particular set of) human preferences through techniques such as reinforcement learning from human feedback @xcite . alignment specifies default model positions and behaviours, which, in principle, gives meaning to evaluations that try to identify the values and opinions reflected in these defaults. in this context, our results may suggest that, on a spectrum from infinite superposition to singular stable persona, the llms we tested fall somewhere in between. on some pct propositions, models expressed the same opinions regardless of how they were prompted. on other propositions, prompting a model better resembled sampling from some wider distribution of opinions. this is consistent with models manifesting stable personas in some settings and superposition in other settings. it is plausible that future models, as a product of more comprehensive alignment, will also exhibit fewer instabilities. ## conclusion multiple-choice surveys and questionnaires are poor instruments for evaluating the values and opinions manifested in llms, especially if these evaluations are motivated by real-world llm applications. using the political compass test (pct) as a case study, we demonstrated that artificially constrained evaluations produce very different results than more realistic unconstrained evaluations, and that results in general are highly unstable. based on our findings, we recommend the use of evaluations that match likely user behaviours in specific applications, accompanied by extensive robustness tests, to make local rather than global claims about values and opinions in llms. while our work may call into question current evaluation practices, we believe that it also opens up exciting new avenues for research into evaluations that better speak to pressing concerns around value representation and biases in real-world llm applications.