Whose Emotions and Moral Sentiments do Language Models Reflect? (2024)

Abstract: Language models (LMs) are known to represent the perspectives of some social groups better than others, which may impact their performance, especially on subjective tasks such as content moderation and hate speech detection. To explore how LMs represent different perspectives, existing research focused on positional alignment, i.e., how closely the models mimic the opinions and stances of different groups, e.g., liberals or conservatives. However, human communication also encompasses emotional and moral dimensions. We define the problem of affective alignment, which measures how LMs’ emotional and moral tone represents those of different groups. By comparing the affect of responses generated by 36 LMs to the affect of Twitter messages written by two ideological groups, we observe significant misalignment of LMs with both ideological groups. This misalignment is larger than the partisan divide in the U.S. Even after steering the LMs towards specific ideological perspectives, the misalignment and liberal tendencies of the model persist, suggesting a systemic bias within LMs.

## introduction the capacity of language models (lms) to generate human-like responses to natural language prompts has led to new technologies that support people on cognitive tasks requiring complex judgements. however, researchers found that lms inherit biases foot_1 from humans, as their views are shaped by online users who produced the pretraining data, feedback from crowdworkers during reinforcement learning from human feedback (rlhf) process @xcite , and potentially, the decisions made by the model developers themselves @xcite . in subjective tasks, such as hate speech detection @xcite , content moderation @xcite , and legal judgement @xcite , these biases may show up as lms adopting the perspectives of one group while excluding others. this may lead to undesirable downstream consequences, ranging from negative user experiences with lms to societallevel inequity, division and polarization. to examine how lms represent differences in perspectives of different groups, existing research has looked at positional alignment: how closely the model's opinions or stances mirror those of different social groups @xcite . using multi-choice survey questions, researchers have demonstrated that language models are misaligned with the us population and represent the perspectives of some demographic groups better than others. however, positional agreement captures just one aspect of alignment. human communication also carries cues to emotions and moral sentimentscollectively referred to as affect-which are integral to social interaction and cohesion @xcite @xcite . while emotions help shape individual's positions and stances on issues, they are distinct constructs. take an example of a specific position -a pro-masking stance. the same position can be expressed using contrasting emotions as in the following two statements: while both statement take a pro-masking stance, the first one expresses positive emotions, and the second negative ones. whose affect, i.e., emotional and moral tone, do language models reflect? our contributions. we define affective alignment as the degree to which a model's emotional and moral tone matches that of people in similar situations. to study this, we analyze two datasets of twitter messages on contentious issues like covid-19 and abortion, disaggregating users by political ideology (liberal or conservative). we prompt 36 lms of varying sizes to generate statements on topics like "covid-19 mask mandates" and "abortion rights and access," then compare the emotions and moral sentiments in the modelgenerated responses to those of twitter users from different ideological groups. we first assess the models' default affective alignment, based on the responses to default prompts that do not include information about a target demographic (persona). our findings suggest that lms show significant misalignment in affect with either ideological group, and the differences are larger than the ideological divide on twitter. moreover, consistent with prior findings @xcite @xcite , all lms exhibit liberal tendencies on topics related to covid-19. next, we assess lms' affective alignment after we provide additional context in the prompt-steer the model-to generate texts from the perspective of liberals or conservatives. the results reveal that steering can help align lms with the target group's affect. however, even after steering, the models remain misaligned, and liberal tendencies of lms cannot be mitigated by steering. we believe that a deep analysis of the affect expressed by existing lms is crucial for building ai systems for greater social good. to the best of our knowledge, our work is the first to systematically assess the affective alignment of lms, which highlights the unequal affective representations of different ideological groups in current lms. we hope that our framework can help guide future research in better understanding lms' representativeness of people from diverse backgrounds on an emotional and moral level. we elaborate the broader impact of the affective alignment in appendix a. clarification on the scope. our work introduces a new task of systematically probing lms' affective alignment with different social groups. we aim to objectively present our finding and offer insights, rather than prescribing optimization. whether a high degree of affective alignment towards each single group is desirable, and whether lms should equally represent each group's affect, are highly dependent on the application context. ## measuring affective alignment figure 1 shows the overall framework. we first prompt lms to generate tweets on some topic us-ing default prompting or steered prompting. we use state-of-the-art classifiers to measure emotions and moral sentiment of both lm-generated tweets and human-authored tweets. the affective alignment is measured as one minus the jensen-shannon distance (jsd) between the distributions of affect. ## related work measuring human-lm alignment lms trained on extensive datasets of human language from the internet, are capable of simulating realistic discourse. to ensure that lms generate text consistent with human values and ethical principles, many recent works have investigated the human-lm alignment. popular frameworks include reinforcement learning with human feedback (rlhf) or ai feedback (rlaif) @xcite @xcite . to measure alignment santurkar et al. (2023) compared lms' opinions with human responses in public opinion polls among various demographic groups and found substantial misalignment. @xcite expanded the study of alignment to a global scale using crossnational surveys and discovered lms' inclination towards certain countries like usa, as well as unwanted cultural stereotypes. simmons (2022) measured lms' moral biases associated with political groups in the united states when responding to different moral scenarios; however, they only evaluate the models' moral responses based on a general statistical finding from previous works that "liberals rely primarily on individualizing foundations while conservatives make more balanced appeals to all 5 foundations". @xcite measured moral foundations of llms using the moral foundations questionnaire (mfq) questionnaire and compared the results of llms to humans. in contrast, our work evaluate the models against affect distributions observed from real-world humangenerated texts on a topic basis. the affect people/models express in open-ended texts is likely to be different from how they answer the closeended questionnaire, and our framework can capture the fluidity and complexity of human affective responses in a way that structured questionnaires might not fully encompass. feng et al. (2023) discovered that pretrained lms do exhibit political biases, propagating them into downstream tasks. in terms of adapting lms to simulate human opinions, @xcite showed that gpt-3 can mimic respondents in extensive, nationally-representative opinion surveys. other researchers have finetuned lms to learn the political views of different partisan communities to study polarization @xcite . to evaluate news feed algorithms, @xcite created multiple lm personas from election data to simulate conversations on social media platforms. @xcite found that most llms show a left-leaning stance on a wide range of issues and llms can be easily manipulated by instruction tuning. ## conclusion our study has explored how lms align with the affective expressions of liberal and conservative ideologies. through the lens of two contentious sociopolitical issues, we discover that lms can mimic partisan affect to a degree, which, nevertheless, is weaker than that between liberals and conservatives in the real world. in addition, lms show liberal tendencies on certain issues, aligning more with the affect of liberals. the misalignment and the liberal tendencies are not solvable by steering. as a first step towards systematically measuring the affective alignment of lms with different social groups, we hope that this study will gather more attention from the research community in understanding the interactions of affect between lms and humans.