LLM Tropes: Revealing Fine-Grained Values and Opinions in Large Language Models (2024)

Abstract: Uncovering latent values and opinions embedded in large language models (LLMs) can help identify biases and mitigate potential harm. Recently, this has been approached by prompting LLMs with survey questions and quantifying the stances in the outputs towards morally and politically charged statements. However, the stances generated by LLMs can vary greatly depending on how they are prompted, and there are many ways to argue for or against a given position. In this work, we propose to address this by analysing a large and robust dataset of 156k LLM responses to the 62 propositions of the Political Compass Test (PCT) generated by 6 LLMs using 420 prompt variations. We perform coarse-grained analysis of their generated stances and fine-grained analysis of the plain text justifications for those stances. For fine-grained analysis, we propose to identify tropes in the responses: semantically similar phrases that are recurrent and consistent across different prompts, revealing natural patterns in the text that a given LLM is prone to produce. We find that demographic features added to prompts significantly affect outcomes on the PCT, reflecting bias, as well as disparities between the results of tests when eliciting closed-form vs. open domain responses. Additionally, patterns in the plain text rationales via tropes show that similar justifications are repeatedly generated across models and prompts even with disparate stances.

## introduction values and opinions embedded into language models have an impact on the opinions of users interacting with them, and can have a latent persuasion effect @xcite . identifying these values and opinions can thus reveal potential avenues for both improving user experience and mitigating harm. recent works have proposed to evaluate figure 1 : we propose to evaluate llm political values and opinions through both generated stances towards propositions and tropes: repeated and consistent phrases justifying or explaining a given stance (two real, identified tropes for llama 3 depicted here). llm values and opinions using surveys and questionnaires @xcite @xcite , as well as by engaging llms in role-playing and adopting the personas of different characters @xcite . however, existing approaches suffer from three notable shortcomings. first, recent work has shown that the responses of llms to survey questions depend highly on the phrasing of the question and the format of the answer @xcite @xcite , calling for a more robust evaluation setup for surfacing values embedded in language models. second, when provided with different personas based on demographic characteristics, llms can reflect the social and political biases of the respective demographics @xcite , highlighting the need for disentangling the opinions embedded into llms and their variation when prompted with demographics. such efforts also aid in aligning language models for different populations and cultures and prevent jailbreaking of llms @xcite . lastly, these evaluations focus primarily on quantifying stances towards the survey questions, ignoring justifications and explanations for those decisions. revealing patterns in such data could offer a naturalistic way to express latent values and opinions in llms. to address these shortcomings, we conduct a large-scale study eliciting 156,240 responses to the 62 propositions of the political compass test (pct) across 6 llms and 420 prompt variations. these prompt variations span different demographic personas: age, gender, nationality, political orientation, and class, as well as instruction prompts, in order to provide a robust set of data for analysis while disentangling demographic features. we propose to perform both coarse-grained analysis at the level of stances, allowing us to quantify political bias based on the pct, as well as fine-grained analysis of the plain text open-ended justifications and explanations for those stances, allowing us to reveal latent values and opinions in the generated text. for fine-grained analysis, we propose to identify tropes in the responses: semantically similar phrases that are recurrent and consistent across different prompts, revealing patterns in the justifications that llms are prone to produce in different settings. an example from our dataset is given in figure 1 , where llama 3 (ai@meta, 2024) when prompted with two different demographic personas (being far left vs. far right) demonstrates both a tendency towards particular stances as well as particular lines of reasoning for those stances. overall, our contributions foot_0 are: ## related work surfacing political biases embedded in nlp tools has been approached previously in the context of word embeddings @xcite and masked language modeling @xcite . as these biases originate in the data the models are trained on @xcite , there is also research on directly tying them back to training data @xcite . recently, with more performant and coherent llms, extracting inherent biases have garnered more attention. this has been explored by eliciting responses from psychological surveys @xcite , opinion surveys @xcite @xcite , and personality tests @xcite , finding that certain llms have a left-libertarian bias @xcite . there has also been research on examining the framing of the generations produced by llms @xcite , and on measuring biases across different social bias categories @xcite . recently, persona based evaluation of political biases has also been attempted to see if models can simulate the responses of populations @xcite @xcite , including in languages other than english @xcite . however, impact of persona based evaluation remains underexplored. furthermore, there is research demonstrating the brittleness, values, and opinions of llms @xcite . our work is most similar to @xcite , who try to answer the question of how to meaningfully evaluate values and opinions in llms by way of the pct. they demonstrate that eliciting open-ended responses from llms can lead to vastly different results than closed-form categorical selections, questioning the validity of biases found through such methods. they argue for more robust and domain specific evaluations. in order to further assess the sensitivity of these evaluations with respect to multiple personas, we conduct a systematic evaluation of variance across 156k generations. further, considering the infeasability of analysing open-ended responses, we present a novel method for conducting such analysis through extraction of tropes from them. ## methodology as discussed, we aim to address three shortcomings in previous work: prompt variations lead to inconsistent responses from llms with respect to survey questions @xcite @xcite , demographic features in the prompt can cause the responses to reflect perceived features of those demographics @xcite , and biases in the plain text justifications have been largely ignored. to do so, we start by generating a large and diverse set of responses to propositions from the pct, 3 which has been used several times in the recent literature around llm political bias @xcite @xcite , providing many responses to each of the propositions with different models, prompts, and instructions. this allows us to perform both coarse-grained analysis of the stances (i.e., what a given model scores on the pct) across different settings, as well as fine-grained analysis through the lens of tropes by uncovering patterns in the generated text across multiple responses to the same proposition (described in §3.2). ## analysis our analysis centers around four research questions addressing the three shortcomings of previous work described in §1: rq1: how do demographic-based prompts impact llm survey responses? rq2: do categorical surveys administered to llms elicit robust results over diverse prompts? rq3: what tropes do llms produce in response to the pct? rq4: do variations in categorical survey responses reflect variations in the tropes? to answer these questions, we generate a large dataset of responses to the pct as described in §3.1, eliciting 26,040 responses for 6 different language models (156,240 responses total). 7 we use the following llms: mistral, mixtral, zephyr, llama 2, llama 3, olmo (seeapp. a for further details). ## discussion and conclusion llms generations contain latent values and opinions when responding to queries, which can have an impact on the users interacting with them. when researchers try to surface these values and opinions, llms are typically prompted to answer survey questions, but our work shows that these answers are sensitive to the output format and personas. the sensitivity, however, is dependent on the persona category, being more sensitive to some (gender, political orientation, class) over others. further, while prior work has shown the default biases, we show that model responses vary substantially and can be steered in terms of political biases through these personas. through our experiments, we show how some models are more prone to this than others, llama2 by doing so, we can work towards creating a more equitable and inclusive society for everyone, regardless of their background or nationality. ultimately, i believe that we need to work towards creating a more equitable society where everyone has access to the same opportunities and resources, regardless of their class or nationality. we need to work towards a more equitable society, where everyone has access to the resources and opportunities they need to thrive, regardless of their background or nationality. as a society, we must work towards creating a more equitable distribution of wealth and resources, regardless of nationality or social class. let's work together to create a more equitable society, regardless of our nationality or class. so, instead of focusing on nationality, we should be working towards a more equitable and just society where everyone has access to the resources and opportunities they need to thrive. therefore, i believe that we need to work towards creating a more equitable society, where everyone has access to the resources and opportunities they need to thrive, regardless of their background or nationality.