Are Large Language Models Consistent over Value-laden Questions? (2024)

Abstract: Large language models (LLMs) appear to bias their survey answers toward certain values. Nonetheless, some argue that LLMs are too inconsistent to simulate particular values. Are they? To answer, we first define value consistency as the similarity of answers across 1) paraphrases of one question, 2) related questions under one topic , 3) multiple-choice and open-ended use-cases of one question, and 4) multilingual translations of a question to English, Chinese, German, and Japanese. We apply these measures to a few large, open LLMs including llama-3, as well as gpt-4o, using eight thousand questions spanning more than 300 topics. Unlike prior work, we find that models are relatively consistent across paraphrases, use-cases, translations, and within a topic. Still, some inconsistencies remain. Models are more consistent on uncontroversial topics (e.g., in the U.S., “Thanksgiving”) than on controversial ones (e.g. “euthanasia”). Base models are both more consistent compared to fine-tuned models and are uniform in their consistency across topics, while fine-tuned models are more inconsistent about some topics (e.g. “euthanasia”) than others (e.g. “Women’s rights”) like our human participants.

## introduction large language models (llms) are increasingly used in value-laden situations, ranging from simulating survey respondents @xcite to aligning llms to particular values @xcite . notably, @xcite and @xcite administer large social surveys to llms, finding that models disproportionately bias toward the values of people in places like silicon valley. nevertheless, in most cases, these works assume that llms have consistent values. we thus focus on the major assumption that llms are consistent with a set of values. to interrogate that assumption, we ask whether a model we detail an unsupervised method to gauge the consistency of models' expressed behavior as a means to quantify what values models have. to do so, we formalize a number of desirable measures of value consistency, assuming that the values latent in an answer to a particular question remain reasonably consistent across (1) paraphrases, (2) multiple-choice and open-ended use-cases, (3) mul-tilingual translations, and (4) across similar questions within a given topic ( §3). while these measures may be used for consistency more broadly, we call them measures of value consistency here as they operate in explicitly value-laden domains. in order to apply these measures, we introduce a novel dataset, valueconsistency, containing more than 8k questions over 300 topics and across four languages ( §4). unlike prior work, we investigate both controversial and uncontroversial topics, compare base models and fine-tuned models, generate countryspecific topics, and study models' consistency over translations. via extensive analyses, we find the following: @xcite contrary to our expectations, large models are reasonably consistent over our measures, being as or more consistent than our human participants (@xmath0. 4 ). (2) across measures, models are more consistent over less controversial questions (fig. 5 ). (3) base models are more consistent compared to their fine-tuned counterparts (fig. 3 ). (4) fine-tuned models, like our human participants, are more consistent on some topics than others; base models are equally consistent (fig. 6 ). ## defining value consistency what do we mean by consistency of values? here, we operationalize value consistency as a measure of four representative similarities over paraphrases, topics (similar questions from the same topic), usecases (e.g. open-ended or multiple choice), and multilingual translations of the same questions. note that this operationalization is not exhaustive; we encourage scholars to propose more measures. ## constructing valueconsistency instead of relying on existing datasets of controversial topics such as surveys @xcite , we sought to provide an extensible, and largely unsupervised, method to generate value-relevant questions. indeed, prior work has used llms to systematically generate, with reliable filtering, the content of datasets for social nlp @xcite @xcite . we thus introduce value-consistency, a dataset of more than 8000 questions across more than 300 topics. tab. 2 breaks down our questions by category and tab. 7 lists a few example topics. 4 in particular, we generated topics, questions relevant to those topics, answers to those questions with their associated stance toward a topic (e.g., "yes" to "do you like cats" indicates support for cats), and paraphrases for those questions. see fig. 2 . we prompted for controversial topics in the united states in english, translating them to chinese, german, and japanese using gpt-4-0613. we did the same for topics in each subsequent country and language, but for the rest only translated to english. 5 we chose these languages because they quality check two of the authors manually read each of the english translations of the unique topics, questions, and answers (although not the paraphrases) for each country and language. we made sure all questions per topic were about the same thing; we deleted any questions (or entire topics) that were too vague and aligned any inconsistent answers. 7 (see tab. 8). we also manually reviewed all paraphrases in english. out of 657 controverand culture is not simply nationality @xcite . our questions do not capture all cultural nuances. 6 our code is available under the mit license here: https: //github.com/jlcmoore/valueconsistency 7 for example, we deleted the topic "education reform" as the generated questions, such as "do you think that the current us education system needs significant reform?" and "do you believe standardized testing is an effective measure of a student's abilities?" were too broad and unrelated. we also recruited human annotators to validate the our dataset in non-english languages, targeting users either fluent in english and each of {chinese, german, japanese}. to judge the quality of our paraphrases, we presented (@xmath2, asking them in english to respond "yes" or "no" to the question, "are all these questions asking the same thing?" annotators rated paraphrases as significantly more equivalent than chance. for the controversiality of our questions, we similarly presented (@xmath3, "how controversial are these questions?" annotators rated questions gpt-4 judged as controversial as significantly more controversial than questions judged as uncontroversial. (see tab. 4.) ## discussion prior work has argued that models either do @xcite or do not @xcite hold certain values. so: are llms consistent over value-laden questions? while the answer is more yes than no, our findings show that the underlying complexity cannot be captured by a binary answer. indeed, unlike prior work @xcite , we have found that large models (>= 34b) are relatively consistent across our measures, performing on par with human participants on topic and paraphrase consistency (fig. 4 ). nonetheless, models' consistency is not uniform. in general, base models are more consistent than their fine-tuned counterparts (fig. 6 ). moreover, base models are more consistently consistent than fine-tuned ones. for example, llama3, like our human participants, is very consistent on "women's rights" but very inconsistent on "euthanasia" while llama3-base does not exhibit such patterns (fig. 3 ). models are more consistent over uncontroversial than controversial questions (fig. 5 ). we also measure how well models can be steered to particular values ( §6.5), showing that models cannot be steered using a common set of values (fig. 9 ). which values do models have? when do we want models to be consistent? while we here note that models are reasonably consistent on our measures of value consistency, we have said little about the particular values models may have. we do not resolve whether it is good or bad that llms are inconsistent on our measures. still, judgement is obviously warranted in some domains, such as when llms consistently bias against certain cultures @xcite . future work should clarify in what domains consistency is or is not warranted @xcite . moving forward, how can we make models more consistent over values? some existing work @xcite ) attempts to answer this in a general way, but more is needed on value-laden domains in particular. can we make models more consistent in some domains than others? in general, we would like to see future work extend to more languages and use cases, as well as connect questions of value consistency to the real world, e.g. models in deployed settings. indeed, the multi-turn conversations possible over long context windows may dramatically shift model behavior in ways we cannot anticipate here @xcite . ## conclusion what does it mean for a model to have a value? answers abound ( §2). the positions models express (and those they cannot) affect people. understanding which values models hold, and the degree to which models hold them, is an important first step in diagnosing and mitigating these potential issues. instead of assuming a fixed set of values like prior work @xcite , we focus on how models tend to answer, namely whether they are consistent over value-laden questions. with a few notable exceptions ( §7), we find that large language models are relatively consistent (and similar in inconsistencies to our human participants) across paraphrases, use-cases, multilingual translations, and within topics ( §3) using a novel dataset, valueconsistency, generated with gpt-4 ( §4).