Are Rules Meant to be Broken? Understanding Multilingual Moral Reasoning as a Computational Pipeline with UniMoral
Authors: Shivani Kumar, David Jurgens
Accepted at: acl25

Are Rules Meant to be Broken? Understanding Multilingual Moral Reasoning as a Computational Pipeline with UNIMORAL

Shivani Kumar
University of Michigan
kshivan@umich.edu

David Jurgens
University of Michigan
jurgens@umich.edu

Abstract

Moral reasoning is a complex cognitive process shaped by individual experiences and cultural contexts and presents unique challenges for computational analysis. While natural language processing (NLP) offers promising tools for studying this phenomenon, current research lacks cohesion, employing discordant datasets and tasks that examine isolated aspects of moral reasoning. We bridge this gap with UNIMORAL, a unified dataset integrating psychologically grounded and social-media-derived moral dilemmas annotated with labels for action choices, ethical principles, contributing factors, and consequences, alongside annotators’ moral and cultural profiles. Recognizing the cultural relativity of moral reasoning, UNIMORAL spans six languages, Arabic, Chinese, English, Hindi, Russian, and Spanish, capturing diverse socio-cultural contexts. We demonstrate UNIMORAL’s utility through a benchmark evaluation of three large language models (LLMs) across four tasks: action prediction, moral typology classification, factor attribution analysis, and consequence generation. Key findings reveal that while implicitly embedded moral contexts enhance the moral reasoning capability of LLMs, there remains a critical need for increasingly specialized approaches to further advance moral reasoning in these models.

1 Introduction

Computational reasoning systems excel at processing structured domains like mathematics (Imani et al., 2023) and commonsense problem-solving (Sap et al., 2020) through logical and probabilistic frameworks (Yu et al., 2024). Moral reasoning, however, introduces multidimensional complexity by requiring the integration of emotional intelligence (Zangari et al., 2025) and ethical principles such as fairness (Schramowski et al., 2019), harm mitigation (Graham et al., 2018), and duty (Ellemers et al., 2019). Consider the canonical dilemma of discovering a lost wallet (Figure 1): Human moral cognition synthesizes perceptual inputs (Haidt, 2001), value-based judgments (Greene, 2007), and post-hoc rationalizations (Nabavi, 2012) into actionable decisions (Walker, 1989). While this integrated pipeline reflects natural human reasoning (Kohlberg, 1963), existing NLP approaches analyze moral decision-making through fragmented methodologies (Hendrycks et al., 2021; Vida et al., 2023), limiting both holistic understanding and cross-cultural applicability.

In this work, we bridge this gap by introducing UNIMORAL, a multilingual dataset designed to capture the phased nature of moral reasoning. Grounded in psychological theories and enriched by real-world social media discourse, UNIMORAL provides annotations across the entire moral reasoning process, covering scenario perception, action selection, ethical judgment, justification through contributing factors, and the consequences of the chosen action. Broadly, to construct UNIMORAL, we present crowd-sourced participants with moral scenarios where participants select preferred actions, justify decisions via follow-up questions, and complete post-annotation moral (Atari et al., 2023)

![Moral Reasoning pipeline: An individual encounters a moral scenario, they list out the potential actions they can take, and select one. The chosen action yields outcomes affecting stakeholders and societal norms. The “Moralsphere” conceptualizes this dynamic interplay between reasoning, action, and societal impact in resolving moral dilemmas.](page_1012_320_388_246.png)

Figure 1: Moral Reasoning pipeline: An individual encounters a moral scenario, they list out the potential actions they can take, and select one. The chosen action yields outcomes affecting stakeholders and societal norms. The “Moralsphere” conceptualizes this dynamic interplay between reasoning, action, and societal impact in resolving moral dilemmas.
and cultural value questionnaires (Hofstede, 1994).

Several studies (Kennedy et al., 2021; Yang et al., 2024; Xu et al., 2025) provide a strong foundation for understanding how moral decisions and values shift across languages and cultures. Building on their findings, our goal is to examine whether LLMs exhibit similar variations in moral reasoning when exposed to different linguistic and cultural contexts. UNIMORAL encompasses six linguistically distinct contexts, Arabic, Chinese, English, Hindi, Russian, and Spanish, enabling us to probe how moral frameworks vary across populations. While UNIMORAL supports diverse applications, in this study we focus on four pivotal research questions to analyze how current LLMs handle moral reasoning: [I] Action Prediction (AP): How does contextual cues, like cultural orientation and individual’s moral values influence computational models’ capability for action prediction in UNIMORAL, and to what extent do these predictions generalize across its six languages? [II] Moral Typology Classification (MTC): Can computational models classify moral actions in UNIMORAL into psychologically grounded categories (e.g., deontological and virtuous) using its hierarchical annotations, and how do these categorizations vary across languages? [III] Factor Attribution Analysis (FAA): To what extent can a model determine the contributing factors dominating a person’s moral decision making, and how do these factors interact across languages? [IV] Consequence Generation (CG): Are computational models capable of generating coherent consequences of scenario-action pairs in UNIMORAL?

Although the AP and CG can be addressed using existing datasets for individual languages, UNIMORAL facilitates cross-linguistic comparison and extends its utility to addressing additional inquiries as posed by the MTC and FAA. Through systematic benchmarking, we demonstrate UNIMORAL’s utility in addressing these questions, revealing nuanced patterns in how cultural narratives and personal values shape moral evaluations. In a nutshell, the contributions of this work can be summarized as:

1. We identify the different stages of moral reasoning and present a systematic, holistic, and psychologically-motivated pipeline for structured computational modeling.
2. We present UNIMORAL\(^1\), a diverse, multilingual, and holistic dataset of moral dilemmas derived from psychological theories and social media, with rich annotations spanning all phases of moral reasoning (perception, judgment, justification, action, and consequence) and individualized moral and cultural profiles derived from participant responses.
3. Through four targeted research questions\(^2\), facilitated by UNIMORAL, we analyze current LLMs and examine the influence of cultural narratives and personal ethical values on shaping their moral reasoning.

2 Morality in NLP

Ethics, in NLP, has garnered significant traction in recent years, with many contemporary studies investigating the notion of morality in hypothetical texts, such as stories (Emelin et al., 2021; Guan et al., 2022), and social media texts, like Reddit posts (Trager et al., 2022) and tweets (Hoover et al., 2020). The tasks coming under the umbrella of “morality in NLP” generally falls under two categories: quantification and judgment.

Moral quantification. This subdomain of tasks typically addresses scenarios where the action of an individual is known, and the computational system is tasked with identifying specific aspects of the action or the individual performing it. For instance, tasks such as moral value identification (Teernstra et al., 2016; Mokhberian et al., 2020; Lan and Paraboni, 2022; Pavan et al., 2023), moral stance detection (Santos and Paraboni, 2019; Roy and Goldwasser, 2021; Botzer et al., 2022), and moral sentiment classification (Mooijman et al., 2018; Kobbe et al., 2020; Roy et al., 2021; Qian et al., 2021) fall into this category. The methodologies employed to tackle such tasks frequently involve the use of lexicons (Anderson et al., 2006; Garten et al., 2016; Alfano et al., 2018), machine learning approaches (Asprino et al., 2022; Hsu et al., 2021), and, more recently, large language models (Alhassan et al., 2022; Alshomary et al., 2022).

Moral judgment. The tasks in this category involve making judgment about either the action taken by an individual (Ammanabrolu et al., 2022; Shen et al., 2022; Yamamoto and Hagiwara, 2014), the consequence of those actions (Komuda et al., 2013; Emelin et al., 2021), or the individuals themselves (Hendrycks et al., 2020; Lourie et al., 2021).

\(^1\)https://huggingface.co/datasets/shivaniku/UniMoral
\(^2\)https://github.com/shivanik96/UniMoral.git
Action judgment tasks are often presented in two formats: choosing the moral action from a set of alternatives (Emelin et al., 2021; Guan et al., 2022), or evaluating whether a completed action was moral (Jin et al., 2022; Hendrycks et al., 2020). However, many existing studies overlook the critical aspect of moral evaluation: assuming only one action is moral and ignoring the decision-maker’s context. In UNIMORAL, we focus on this personalization aspect, ensuring that moral reasoning considers both actions and contextual factors.

Morality across languages and cultures. Contributions, like Moral Foundations Theory (Graham et al., 2018) and its extensions (Hopp et al., 2020) offer lexicons for analyzing moral sentiment but frequently prioritize Western-centric frameworks. Cross-cultural adaptations, such as the Japanese MFD (Matsuo et al., 2019), and multilingual resources like MoralConvIta (Stranisci et al., 2021), alongside studies of cultural variations in moral priorities (Atari et al., 2023) highlight the importance of linguistic and cultural diversity, paving the way for more inclusive approaches.

The moral reasoning pipeline. Just as a mathematical problem is solved by following a step-by-step approach (Imani et al., 2023), moral reasoning involves sequential steps to arrive at an appropriate action for a scenario. While numerous psychological studies discuss the phases of moral reasoning (Kohlberg, 1963; Carpendale, 2009; Haidt, 2001; Greene, 2007; Nabavi, 2012), no work in NLP, to the best of our knowledge, has translated these psychological concepts into a computational format to explore moral reasoning as a systematic sequence.

To this end, we construct a pipeline, as shown in Figure 1, that captures the various stages of moral reasoning. According to Rest’s four-component model (Narvaez and Rest, 1995) and Haidt’s Social Intuitionist Model (Haidt, 2001), the pipeline begins with scenario assessment. Subsequently, possible actions are contemplated, with each influenced by various factors, akin to the Dual-Process Theory of Moral Judgment (Greene, 2007). After identifying possible actions, a decision is made. Similar to real-life situations, every action has consequences; in moral reasoning, these consequences can be judged as moral or immoral, providing a learning opportunity as outlined in Bandura’s social learning theory (Nabavi, 2012).

Throughout these stages, we also examine aspects of each phase, such as moral values and sentiments. We refer to this overarching process as the “Moralsphere” since it spans all phases of moral reasoning. These processes within the moral pipeline collectively function and interact to enable informed moral decision-making. To support the computational study of this pipeline, we introduce UNIMORAL, which provides comprehensive annotations for all the phases of moral reasoning.

3 Constructing UNIMORAL

This section outlines the data collection, annotation framework, and analysis of UNIMORAL.

3.1 Collecting Data for Annotation

Broadly, the data construction pipeline encompasses the following five steps: 1) generating scenarios rooted in psychological theories, 2) determining the most probable action options within these scenarios, 3) gathering moral dilemmas from Reddit along with their corresponding action options, 4) translating the collected data into target languages for study, and 5) obtaining annotations through crowd-sourcing. We provide an in-depth discussion of each of these steps below.

Psychologically grounded scenarios and their actions Numerous psychology studies have examined human morality by presenting participants with morally charged dilemmas and inquiring about their actions (Colby et al., 1983; Rest, 1979; Lind, 2008). These studies curate scenarios to ensure decisions require moral reasoning rather than mere logic. We focus on three prominent theories: the Moral Judgment Interview (MJI) (Colby et al., 1983), the Defining Issues Test (DIT) (Rest, 1979), and the Moral Competence Test (MCT) (Lind, 2008). Together, these theories provide 18 psychological scenarios—seven from both MJI and DIT, and four from MCT—designed to trigger moral reasoning. After reviewing them, we identified two as repetitive, resulting in 16 unique dilemmas or our “seed scenarios” (\( S \)). More details on these theories and scenarios are present in Appendix A.1.

Additionally, we draw upon established psychological theories that identify the elements affecting human decision-making and summarize nine major contributing factors as \( C = \{ 'Emotions', 'Moral', 'Culture', 'Responsibilities', 'Relationships', 'Legality', 'Rules', 'Politeness', 'Sacred values' \} \). Specifically, the contributing factors of moral, culture, relationships, and sacred values come from the work by Graham et al. (2018) which
talks about nativism, cultural learning, intuitionism, and pluralism. Furthermore, the study conducted by Damasio (1996) suggests that past experiences, which are often influenced by emotions, morals, responsibilities, and relationships, contribute towards the decision making in a given situation. Similarly, Lessnoff (1971) talks about the framework of social contracts, emphasizing duties and responsibilities towards society, giving rise to the contributing factors of responsibilities, legality, and rules. Finally, the Cultural Dimensions Theory (Hofstede, 1994) highlights how culture impacts moral decisions in both workplace and broader contexts, contributing to the factors of culture, politeness, relationships, and sacred values. To generate new scenarios that emulate the seed scenarios, we use Llama-3.1-70B Instruct (Meta, 2024), prompting it to create scenarios similar to \( S \) with contributing factors derived from \( C \). That is, our new set of scenarios, \( S_e^p \), is defined as \( S_e^p = \{ f(s, c) \mid \forall s \in S, \forall c \in C \} \), resulting in a total of 144 scenarios (\(|S| \times |C|\)). We assessed a random sample of generated scenarios and actions based on four criteria: distinctness from the seed scenarios, clear presence of the contributing factor, presence of a moral dilemma, and viable mutually exclusive action options. For each of these new scenarios, we prompt the language model to generate the two most probable mutually exclusive actions, denoted as \( A_e^p = \{ (a_1^{s_e}, a_2^{s_e}) \mid \forall s_e \in S_e^p \} \), which we then present to the annotators. More information about what prompts we use and examples of scenario-action pairs generated, see Appendix Section A.6.

Reddit scenarios and actions. To enhance the utility of UNIMORAL, we extend beyond hypothetical scenarios by incorporating real-life examples of moral dilemmas sourced from Reddit. We focus on subreddits that frequently feature moral judgments, namely r/AmItheAsshole, r/moraldilemmas, r/AITAH, r/TwoHotTakes, and r/AmIOverreacting. Posts made to these subreddits are carefully filtered and rephrased to have standard formatting (see Appendix A.2 for details). To generate our data, we prompt the Llama3.3-70B to rephrase the described scenario as a moral dilemma and generate two mutually exclusive actions applicable to that scenario. Through this methodology, we extract \( \sim 400k \) Reddit scenarios. We randomly select 10k scenarios from the paraphrased Reddit data and perform topic modeling using LDA (Blei et al., 2003) to identify 200 topics. To diversify the types of dilemmas in our data, we cluster the scenarios into 200 clusters using \( k \)-means (Hartigan and Wong, 1979) on the scenarios’ topic distributions. We then select the centroid of each cluster as a representative scenario, resulting in a collection of 200 Reddit-based dilemmas, denoted as \( S_e^r \), along with their corresponding actions, \( A_e^r \). By combining \( S_e^r \) with \( S_e^p \), we obtain the final list of moral scenarios \( S_e^m = \{ S_e^p \cup S_e^r \} \) and, similarly, actions \( A_e^m = \{ A_e^p \cup A_e^r \} \).

Adding multilingualism. In this study, we create moral and cultural profiles of the participants using standardized questionnaires. Specifically, we employ the Moral Foundations Questionnaire 2 (MFQ2) (Atari et al., 2023) and Hofstede’s Value Survey Module (VSM 2013) (Hofstede, 1994) to capture various moral and cultural dimensions (see Appendix Section A.4 for more details). These questionnaires, originally available in English, contain translations in several other languages as well. From the translations, we select five languages—Arabic, Chinese, English, Russian, and Spanish—for which both, MFQ2 and VSM, have translations, enabling the study of cultural and moral value variations across these languages. Additionally, to incorporate views from South Asia as well, we manually translate these questionnaires into Hindi using the standard method of translation and back-translation (Brislin, 1970). Consequently, we have the questionnaires available in six languages for our study. Next, we translate the collected scenarios \( S_e^m \) and actions \( A_e^m \) into these languages using the large version of the SeamlessM4t-v2 model (Seamless Communication et al., 2023), giving us \( S_x^m = \{ T_{c-x}(S_e^m) \} \) and \( A_x^m = \{ T_{c-x}(A_e^m) \} \) where \( x \in \{ A, C, H, R, S \} \) where \( A, C, H, R, S \) stands for Arabic, Chinese, Hindi, Russian, and Spanish, respectively, and \( T \) is the translation function. See Appendix Table 3 for translation examples. These translations are manually verified to ensure quality for our crowd-sourced collection.

3.2 Crowd-sourced Annotation

After establishing \( S^m \) and \( A^m \), we initiate a crowd-sourced data collection process to obtain annotations for each scenario-action pair in our dataset, \( \{ (s_i^m, (a_1^{m_i}, a_2^{m_i})) | 1 \leq i \leq |S^m| \} \). We solicit input from annotators on the following points:
1. Which action would you choose?
2. Explain your choice and its consequence.
3. How well does it capture ethical principles?
4. What factors contributed to your decision?
(a) Actions have almost equal distributions across languages.

(b) Ethical preferences in action choices show correlation with annotators’ first language, along with global trend.

(c) Moral decisions are influenced by varying factors, such as emotions and legal frameworks, across languages.

(d) Moral and cultural values vary across languages as revealed by MFQ2 and VSM assessments.

(e) The TSNE plot reveals moral and cultural (dis)similarities between languages.

Figure 2: UNIMORAL at a glance. [Abbreviations – PDI: Power Distance, IDV: Individualism, MAS: Masculinity, UAI: Uncertainty Avoidance, LTO: Long Term Orientation, IVR: Indulgence vs Restraint]

5. Which emotion(s) influenced your decision?
6. What values shaped your decision?
7. Any alternative action considerable?
    a. How well does it capture ethical principles?

Questions 3 and 7a assess the ethical frameworks guiding the annotator’s chosen action for a scenario (e.g. finding a wallet), categorized as following rules (e.g. handover the wallet to police), doing good for the majority (e.g. donate money to charity), respecting people’s rights (e.g. return wallet to owner), and acting with good character (e.g. return the wallet and check for other missing items.). These correspond to the four main ethical frameworks in moral psychology: deontology, utilitarianism, rights-based, and virtue ethics (Kohlberg, 1963). Annotators rate each principle on a scale of 1 to 5 based on its relevance to their decision. Question 4 asks annotators to evaluate the influence of each contributing factor \( C \) on their decision-making, also on a scale of 1 to 5. If emotion influenced their choice, they specify which from Plutchik primary emotions (Plutchik, 1980). After completing their scenario evaluations, annotators fill out the MFQ2 and VSM to capture their moral values and cultural dimensions, respectively. Additionally, we collect demographic details and a free-text self-description (excluding personal information) to serve as their persona.

<table>
  <tr>
    <th>Study type</th>
    <th># Langs</th>
    <th># Sc/lang</th>
    <th># Ann/sc</th>
    <th># Inst/lang</th>
  </tr>
  <tr>
    <td>Extensive</td>
    <td>6</td>
    <td>194</td>
    <td>3</td>
    <td>582</td>
  </tr>
  <tr>
    <td>Compact</td>
    <td>6</td>
    <td>294</td>
    <td>3</td>
    <td>882</td>
  </tr>
  <tr>
    <td></td>
    <td>6</td>
    <td>488</td>
    <td>3</td>
    <td>1464</td>
  </tr>
</table>

Table 1: Language-wise statistics for UNIMORAL.

Study type. Gathering information for the complete set of eight questions, as specified above, demand considerable time and effort from the annotators. Consequently, each annotator could handle only a limited number of scenario-action pairs. To address this issue, we structure our study in two distinct approaches: extensive and compact. In the extensive annotation, annotators are required to respond to all eight questions associated with a given scenario. In contrast, the compact study involves responding only to the initial question, wherein annotators select their preferred action and proceed to the next scenario. This method allows us to collect more data per moral and cultural questionnaires in a shorter time frame. The extensive data collection contains a set of 144 psychological scenarios alongside 50 Reddit-based dilemmas while the compact study consists of another set of 144 psychological scenarios and the remaining 150 Reddit dilemmas. Table 1 illustrates the language-wise statistics for
UniMORAL. As can be observed, each language contains a total of 1464 instances, making the total number of instances in UniMORAL across the six languages as 8784.

Platform specific information. We construct our annotation platform using Potato (Pei et al., 2022) and host our crowd-sourced study on Prolific.com, initiating the process with a pilot of 100 scenarios in English and Chinese to assess the quality and distribution of the collected data. During this phase, we observe minimal to no difference between the ‘legal’ and ‘rule’ contributing factors, leading us to combine them into a single category (‘laws’) for the final annotation process, giving us \( C' \) such that \( |C'| = 8 \). After confirming that the data quality met our standards, we proceed with twelve studies—one extensive and one compact for each of the six languages. In the extensive study, each annotator evaluated seven scenarios, whereas in the compact study, they assessed thirty scenarios. Each scenario was reviewed by three annotators. Ultimately, UNIMORAL comprises a total of 582 instances with extensive annotations and 882 for the compact version in each language, culminating in a total of 5256 instances across the entire dataset. Further details on the data collection process, screenshot of the annotation framework, and data statistics, can be found in Appendix Section A.3 and A.5.

3.3 Data Analysis

As outlined in Section 3.2, we collect eight types of labels in the extended annotations to explore key aspects of moral reasoning. These labels include understanding preferred actions, factors influencing moral reasoning, and examining variations across languages and cultures. Following, we describe aggregated trends across languages to highlight general variation in preferences.

What kind of actions do people prefer? We plot the distribution of preferred actions across all languages in our dataset, finding an almost equal split, which indicates variability in preferences (Figure 2a). Analyzing the primary ethical principles reveals global trends and a correlation with the annotators’ first language (Figure 2b). For example, all language groups rate virtue highly, but Arabic and Hindi speakers also prioritize right-based ethics highly, focusing on social justice. In contrast, Spanish speakers emphasize utilitarianism, valuing collective well-being, while Russian speakers lean more toward deontology, reflecting duty-based ethics. These differences may stem from cultural variations in moral philosophy.

What are the factors affecting moral reasoning? Moral decisions are often influenced by factors like emotions, culture, legal frameworks and sacred values. Figure 2c shows the impact of these factors across the six languages. While morality is the most significant factor for all languages, sacred values are more influential for Hindi and Arabic speakers or those with high deontological tendencies. Additionally, Spanish speakers value the role of the action taker, while Chinese speakers prioritize politeness. This reflects cultural variations in moral evaluation, with Spanish speakers prioritizing agency in decision-making and Chinese speakers emphasizing social harmony and respect.

Why do different people take different actions? While speakers of various languages prefer different ethical principles, we explore distinctions based on their moral and cultural values. Figure 2d shows aggregate scores from the MFQ2 and VSM assessments (information regarding how these are calculated is present in Appendix Section A.4), revealing clear differences: Arabic speakers are more attuned to the ‘purity’ foundation, while Spanish speakers prioritize ‘equality’ more than English speakers. Additionally, English and Russian speakers exhibit higher ‘individualism,’ correlating with stronger deontological tendencies. A TSNE plot, made by combining the moral and cultural information, in Figure 2e highlights similarities between English, Spanish, and Chinese speakers, and their differences from Arabic and Hindi speakers, reflecting cultural and linguistic influences. Further, Figures 3a and 3b show the most prominent values of morals and culture preferred by each country’s annotators. See Appendix Section A.5 for details.

4 Experimental Analysis

While numerous tasks can be accomplished using UNIMORAL, we focus on four core concepts of moral reasoning: [I] Action Prediction (AP), [II] Moral Typology Classification (MTC), [III] Factor Attribution Analysis (FAA), and [IV] Consequence Generation (CG). Considering the proposed pipeline (Figure 1), our objective is to explore all the phases of moral reasoning and determine how UNIMORAL can be used to study these phases.
(a) Country-wise distribution of most prominent moral values collected via MFQ2 in our study.

(b) Country-wise distribution of most prominent cultural principles collected via VSM in our study.

Figure 3: Distribution of moral values and cultural dimensions across countries from UniMORAL.

4.1 Experimental Setup

We use three large language models: Phi-3.5-mini Instruct (Abdin et al., 2024), Llama-3.1-8B Instruct (Meta, 2024), and DeepSeek-R1-Distill Llama-8B (DeepSeek-AI et al., 2025), for analyzing the four questions. Additional details, including prompt selection, are provided in Appendix Section A.6.

[I] Action Prediction. With the growing interest in agent-based modeling (Gao et al., 2024), and synthetic annotations (Ivey et al., 2024b), it becomes critical that LLMs are able to mirror behavior of certain groups. Consequently, we test whether an LLM, when provided with information about an individual’s values, can replicate their decision-making process. To do this, we focus on three key aspects from our annotation phase: (1) moral values from MFQ2 (\( m \)), (2) cultural principles from VSM (\( c \)), and (3) self-descriptions, or persona, providing an alternative way of capturing the person’s value framework via lived experience (\( p \)). Additionally, we test an alternative approach using few-shot learning (\( fs \)), where past decisions guide the model in predicting future responses. The four attributes (\( m, c, p, fs \)) serve as inputs to three different LLMs, which select the most appropriate action \( a_i^* \) given a scenario \( s_i^m \) and its possible actions (\( a_1^{m_i}, a_2^{m_i} \)). The task is framed as: \( a_i^* = \arg\max_{j \in \{1,2\}} P(a_j^{m_i}|s,x) \), where \( x \in \{m, c, p, fs\} \) and \( P \) denotes the conditional probability. Weighted F1-score acts as our primary metric to capture class variability.

[II] Moral Typology Classification. As outlined in Section 3.2, annotators rated the ethical principles of their chosen actions on a Likert scale. This experiment tests whether an LLM can predict these principles by comparing its predictions to the ground truth, which is based on the highest-rated principle(s). If multiple principles share the highest rating, they are all included. The LLM is prompted to identify the ethical factor influencing the user’s choice, and its prediction is deemed correct if it matches any principle in the ground truth set. We consider the four contextual cues of \( \{m, c, p, fs\} \), similar to AP to evaluate the LLM’s performance. In this approach, the few-shot examples are constructed by considering the ethical principle selected by the annotator for another scenario-action pair. Formally, \( t_i^* = \arg\max_{t \in T} P(t|s_i^m, (a_1^{m_i}, a_2^{m_i}), a_i^*, x) \), where \( x \in \{m, c, p, fs\} \). The weighted F1-score is again employed as the metric of choice.

[III] Factor Attribution Analysis. Moral decisions are influenced by factors such as emotions, responsibilities, and legal considerations. To capture these influences, annotators rated 8 contributing factors (\( F \)) on a scale of 1 to 5, as outlined in Section 3.2. This analysis investigates whether an LLM can accurately identify these factors when given contextual information about the annotator (\( m, c, p, fs \)). The few-shot examples are created using the contributing factors selected by the annotator in another scenario-action pair. The LLM’s task is to predict the most significant factor influencing the decision. If the prediction matches the ground truth—determined by selecting the highest-rated contributing factor (similar to MTC)—it is considered correct. Formally, this is represented as \( f_i^* = \arg\max_{f \in F} P(f|s_i^m, (a_1^{m_i}, a_2^{m_i}), a_i^*, x) \), where \( x \in \{m, c, p, fs\} \). Because this classification involves 8 potential classes, the weighted F1-score is chosen as the preferred metric.

[IV] Consequence Generation. For this task, we evaluate the ability of an LLM to generate the consequence for a selected action, given a moral dilemma. For each language, we compile all con-
Figure 4: Models perform best in English, Spanish, and Russian while struggling for Arabic, Chinese, and Hindi as shown by their language-specific performance. The scores are average weighted F1 scores for AP, MTC, and FAA, and BERTScore for C6. Dotted line represents random performance for each task.

sequences provided by annotators for a scenario serves as the ground truth set for that specific scenario-action pair. The LLM is then prompted to generate a consequence for that scenario-action pair. LLM outputs are scored according to the maximum similarity with any ground truth consequence for that scenario. Formally, this task can be defined as \( c_i = G(s_i^m, a_i^s) \), where \( G \) represents the generative function of the LLM. As detailed in Appendix Section A.7, upon manual observations and from previous studies (Ivey et al., 2024a), it is observed that LLMs tend to generate longer text when compared with human annotators. While this results in low syntactic similarity with the ground truth, it, in no way, means that the consequences generated are bad. Consequently, to ensure a semantically meaningful comparison, we use multilingual BERTScore (Zhang et al., 2020) as our evaluation metric of choice, as it emphasizes semantic similarity rather than exact word matches.

4.2 Experimental Results

We evaluate the models across three dimensions: language-specific performance, assessing their effectiveness across languages for AP, MTC, FAA, and C6; contextual-cue-specific performance, examining the impact of cues (\( p, m, c, fs \)) on AP, MTC, and FAA; and Reddit vs. psychological scenario performance, comparing model performance on Reddit-derived and hypothetical scenarios. In the following paragraphs we highlight the important results, while the full set of results can be found in Appendix Section A.7.

Language-specific performance. To evaluate language-specific performance, we compute the average scores across contextual cues (\( m, c, p, fs \)) and across models for all tasks. Figure 4 presents these results, highlighting substantial variability across languages. English consistently ranks among the highest-performing languages, alongside Spanish and Russian, across all four tasks. In contrast, models exhibit significantly lower confidence in Arabic and Hindi. These disparities can be attributed to factors such as the availability of high-quality training data and linguistic complexity. While English and Spanish benefit from extensive resources (especially in terms of moral datasets) and structural similarities, Arabic and Hindi face challenges related to data scarcity, dialectal variation, and complex morphology.

Contextual cue specific performance. In this section, we examine the impact of different contextual cues (\( m, c, p, fs \)) on task performance. Figure 5 presents the average weighted F1 score, aggregated across languages and models, to illustrate how these cues influence capabilities in AP, MTC, and FAA. For AP, explicitly providing moral information results in the highest performance, with persona-based inputs following closely. However, the performance of pretrained LLMs is not significantly different from chance. This suggests that while the self-descriptions provided by annotators make effective proxies for moral reasoning, LLMs still struggle to internalize ethical principles, often relying on surface-level patterns rather than genuine moral understanding. In MTC, few-shot examples prove to be the most influential, and the only cue to give statistically better performance from chance, in determining the ethical principle guiding the selected action, followed by moral values and user persona. Similarly, in FAA, persona and few-shot examples play a crucial role in helping the model understand individuals and accurately identify the factors influencing their decisions. While FAA performs worse than AP and MTC, it remains significantly above chance, likely because identifying responsible factors relies more on surface linguistic patterns than deep moral reasoning. These results highlight moral reasoning as a challenge for LLMs, which future research—enabled by datasets like UNIMORAL—can help address. Further, while providing the user’s persona enhances LLMs’ ca-
Figure 5: Contextual-cues like moral values and persona help LLMs make better moral decisions. The scores are average weighted F1 scores. Dotted line represents random performance for each task.

capability in moral reasoning, it may not necessarily do so for general reasoning (Huang et al., 2024; Zheng et al., 2024), potentially leading to cognitive inconsistency. This opens a key direction for future research: while humans may reason similarly across moral and general domains (Bryant et al., 2016), the same may not apply to LLMs, highlighting the need to examine how their moral reasoning diverges from general reasoning.

Reddit vs. psychological scenario performance. Are models better able to morally reason about the real-world scenarios from Reddit versus hypothetical psychological scenarios? No. Across all tasks and languages, models perform consistently better on psychologically grounded scenarios than on Reddit-based dilemmas (Figure 6). While the performance difference is modest in AP and CG, it becomes more pronounced in MTC and FAA. This disparity likely stems from the structured and controlled nature of psychologically grounded scenarios, which provide explicit cues that help models isolate and interpret ethical principles. In contrast, Reddit-based dilemmas introduce real-world noise, ambiguity, and implicit cultural or situational biases, making moral reasoning more challenging—particularly in tasks like MTC and FAA, which require precise, context-aware judgment.

Figure 6: Models perform better on psychologically grounded scenarios than on Reddit-based dilemmas across all tasks and languages. The scores are average weighted F1 scores for AP, MTC, and FAA, and BERTScore for CG. Dotted line represents random performance for each task.

5 Conclusion

Moral reasoning is a complex process and here we introduce UNIMORAL to unify the multiple strands of NLP research on moral reasoning. UNIMORAL is a holistic multilingual dataset covering the full moral reasoning pipeline—from scenario perception to consequence evaluation—across six linguistically and culturally diverse contexts. It combines psychologically grounded dilemmas with real-world examples from social media, providing annotations on action choices, ethical principles, contributing factors, and consequences, enriched with annotators’ moral and cultural profiles. Our analysis reveals key insights: (1) models exhibit significant performance disparities across languages, (2) explicit contextual cues—such as moral values and persona descriptions—greatly enhance performance, emphasizing the role of contextual awareness in ethical AI, and (3) tasks like moral typology classification and factor attribution remain challenging, exposing gaps in models’ ability to reason about the broader effects of moral actions. These gaps need to be addressed to further advance moral reasoning in language models. One potential approach could be the integration of domain-specific ethical frameworks that are tailored to diverse cultural and contextual settings (Jiao et al., 2025; Oyinloye, 2021). These frameworks would guide the model’s reasoning processes in a more nuanced and culturally aware manner. Additionally, incorporating continuous learning mechanisms could help models adapt to evolving moral norms and values, improving their reasoning over time (Tennant et al., 2023). These approaches, along with better data diversity (Wang et al., 2023) and more comprehensive ethical training (Divakaran et al., 2022), could help create models that engage with moral reasoning more effectively across languages and contexts.

While this paper explores four verticals of moral reasoning, UNIMORAL enables further studies on cross-cultural moral generalization, bias detection, and moral or cultural value quantification.
6 Limitations

In this work, we introduce the UNIMORAL dataset, which includes annotations capturing action preferences, ethical justifications, and decision factors alongside individual moral and cultural profiles. While UNIMORAL has significant potential for both morality-related and general NLP tasks, this study serves as an initial exploration. Rather than attempting an exhaustive analysis of all possible applications, we focus on four exploratory tasks and evaluate existing systems on these tasks without fine-tuning.

The scope of UNIMORAL is influenced by the availability of the MFQ2 and the VSM across different languages. Since these questionnaires have only been translated into a limited number of languages, and due to our team’s restricted proficiency in certain languages, we conducted annotations in six languages only. Future research can expand on our framework to extend UNIMORAL to additional languages. Further, we use automated translation for moral dilemmas, which may miss subtle cultural and linguistic nuances. To mitigate this risk, we manually inspect translations where, while verifying the correct translation, the evaluator also makes sure that no cultural background from the scenario is lost. However, we recognize that, in rare cases, subtle cultural artifacts may still persist, but they do not affect annotations, since annotators rate each scenario’s text (in comparison to some other approach that would use the same annotations across different translations). That said, future work can still include more thorough human validation.

Additionally, our data collection was conducted on Prolific, meaning that the dataset reflects the perspectives of those who choose to participate on this platform. To ensure diversity, we take deliberate steps to collect moral and value-based information from a broad range of participants. However, as with any dataset, it may not fully capture the moral reasoning of the global population. Future work can further expand participation to enhance representation across different cultural and linguistic backgrounds.

7 Ethical Considerations

To construct UNIMORAL, we gathered data from crowd-sourced workers while ensuring that all information remained strictly anonymous. We employed Prolific and the Potato framework for data collection, both of which rigorously anonymize data by assigning each annotator a unique key. All data recorded for an annotator is linked to this key, with no names associated with the dataset. We obtained informed consent from all participants and ensured prompt and fair compensation for those who completed our study. Additionally, the data collection process was deemed exempt by our institution’s ethics review board.

8 Acknowledgements

We would like to thank Rohan Raju, Haotian Zhang, Nasanbayar Ulzii-Orshikh, and Karla Mercado for their help in verifying the Arabic, Chinese, Russian, and Spanish translations of UNIMORAL. We are also grateful to the members of the Blablablab who took the time to test our annotation platform and provide valuable feedback. We also thank the anonymous participants on Prolific for their time and thoughtful responses. This work is supported by DSO National Laboratories and we thank them for their feedback.

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A Appendix

A.1 Seed Scenario Development

In order to collect our initial set of scenarios that elicit the moral reasoning pipeline in an individual, we consider the scenarios discussed in three psychological theories as discussed below. We show the collected 16 scenarios in Table 2.

Moral Judgment Interview (MJI). The Moral Judgment Interview (Kohlberg, 1963), developed by Lawrence Kohlberg, is a structured interview method used to assess an individual’s moral reasoning based on their responses to hypothetical moral dilemmas. Participants are asked to justify their decisions, and their reasoning is evaluated according to Kohlberg’s six-stage theory of moral development.

Defining Issues Test (DIT). The Defining Issues Test (Rest, 1979), introduced by James Rest, is a standardized multiple-choice assessment designed to measure moral reasoning through the lens of neo-Kohlbergian theory (Kohlberg, 1963). Unlike the MJI, which relies on open-ended responses, the DIT presents participants with moral dilemmas and asks them to rank predefined considerations based on their importance in decision-making.

Moral Competence Test (MCT). The Moral Competence Test (Lind, 2008), developed by Georg Lind, assesses moral competence as the ability to apply moral principles consistently across varying contexts. Unlike the MJI and DIT, the MCT evaluates both moral orientation and consistency in reasoning by presenting respondents with moral dilemmas and asking them to rate arguments for and against different positions. It emphasizes cognitive-affective integration, reflecting how individuals balance moral ideals with practical decision-making. The MCT’s design makes it a useful tool for studying moral competence development and the effectiveness of moral education programs.
<table>
  <tr>
    <th>Theories</th>
    <th>Moral Dilemma</th>
  </tr>
  <tr>
    <td rowspan="6">Moral Judgment Interview</td>
    <td>Heinz’s wife is dying from a particular type of cancer. There is a drug that might save her, but it is very expensive, and Heinz cannot afford it. The pharmacist who discovered the drug refuses to sell it for any less or to let Heinz pay later. Heinz is considering breaking into the pharmacy to steal the drug.</td>
  </tr>
  <tr>
    <td>A man is traveling with his sick father, who is dying. The father begs his son to end his suffering by giving him a fatal dose of medicine. The son is torn between ending his father’s pain and the moral implications of killing him.</td>
  </tr>
  <tr>
    <td>A drug addict is considering stealing money from his family to buy drugs. He knows that if he doesn’t get his fix, he will suffer severe withdrawal symptoms.</td>
  </tr>
  <tr>
    <td>A judge is faced with a difficult decision. A man has committed a minor crime but is a significant public figure. Sentencing him to prison could lead to public unrest and negative consequences for society.</td>
  </tr>
  <tr>
    <td>A doctor has five patients in critical condition, each requiring a different organ transplant to survive. A healthy person walks into the hospital for a routine check-up. The doctor realizes that this person could save the five patients if their organs were harvested.</td>
  </tr>
  <tr>
    <td>Two prisoners are accused of a crime. The authorities offer each prisoner a deal: if one testifies against the other, the testifying prisoner will go free while the other receives a harsh sentence. If both remain silent, they both receive moderate sentences. If both testify against each other, both receive harsh sentences.</td>
  </tr>
  <tr>
    <td rowspan="7">Defining Issues Test</td>
    <td>A trolley is headed towards five people tied up on the tracks. You are standing next to a lever that can switch the trolley to another track where only one person is tied up.</td>
  </tr>
  <tr>
    <td>An escaped prisoner has lived an exemplary life for many years but is discovered and arrested. The dilemma is whether he should be sent back to prison.</td>
  </tr>
  <tr>
    <td>A reporter must decide whether to publish a controversial story that could cause public unrest but would expose a significant injustice.</td>
  </tr>
  <tr>
    <td>A school board must decide whether to allocate limited resources to a special education program or a program for gifted students.</td>
  </tr>
  <tr>
    <td>A doctor must decide whether to administer a high-risk treatment to a terminally ill patient. The treatment could either extend the patient’s life or cause severe side effects.</td>
  </tr>
  <tr>
    <td>A father is a widower with two young children. He must decide whether to remarry for the sake of his children, despite personal reservations.</td>
  </tr>
  <tr>
    <td>A reporter knows of a scandal involving a public official. Publishing the story could harm innocent people but also serve the public interest.</td>
  </tr>
  <tr>
    <td rowspan="3">Moral Competence Test</td>
    <td>A group of workers goes on strike to demand higher wages. The strike causes significant disruption to the company and the public.</td>
  </tr>
  <tr>
    <td>A prestigious school has limited spaces and must decide whether to admit a talented student from a disadvantaged background or a student with excellent academic records but from a well-off family.</td>
  </tr>
  <tr>
    <td>A police officer must decide whether to enforce a law that they believe is unjust but is required by their duty to uphold the law.</td>
  </tr>
</table>

Table 2: Seed Scenarios or moral dilemmas extracted from the Moral Judgment Interview, Defining Issues Test, and Moral Competence Test theories.

A.2 Reddit Moral Dilemma Preprocessing

Reddit contains multiple communities where users submit posts describing a moral dilemma and asking for help or describing a dilemma and what action the user took and asking for judgment. One of the most well-known of these is r/AmItheAsshole which attracts hundreds of posts or more per day. We curate a dataset of dilemmas by synthesizing dilemmas from the real-world scenarios described by users in ‘AmItheAsshole’, ‘moraldilemmas’, ‘AITAH’, ‘TwoHotTakes’, and ‘AmIOverreacting’. Some scenarios are too short so we restrict our candidate scenarios to those with at least 50 words. To avoid dilemmas that are Reddit-specific or require access to external material to understand, we exclude posts that mention Reddit, another subreddit, or a URL. Some users come back to provide additional details or updates using the edit function-

![Distribution of nationalities of the Prolific participants who participated in our studies. The size of the circle is proportional to the number of participants from that country.](page_1042_1342_388_180.png)

Figure 7: Distribution of nationalities of the Prolific participants who participated in our studies. The size of the circle is proportional to the number of participants from that country.

ality and leave a note “EDIT:”; we anticipated that these might be more challenging to paragraph due to the more complex post structure so we remove any posts that have been explicitly edited.
Figure 8: Data collection process for UNIMORAL. Final data comes from a mix of psychological scenarios and Reddit based dilemmas.

A.3 UNIMORAL Annotation Framework

We collect UNIMORAL through crowd-sourcing, wherein we use Potato (Pei et al., 2022) to develop our annotation platform and use Prolific to deploy it. Each annotator is shown an introduction page before they begin the annotation process which described their task in the annotation process and collect their informed consent. Figure 9a shows the introduction and consent page that we show to the annotator to prime them for the study, while Figure 9b shows how the annotation framework looks to the Prolific participant.

For each language, up to 113 participants were enrolled to perform annotations for both the extensive and compact studies. We used Prolific’s internal language screeners, which filters participants based on our specific requirements. For each language (e.g., language X), we screen participants to ensure they are 1) fluent in language X, and 2) have language X as their first language, which ensures cultural variability. Participants were compensated at an average rate of $13 per hour for their annotation work. Given the multilingual nature of our data collection, we staggered the study start times across different time zones to recruit participants from regions where the target languages are commonly spoken. For example, the Chinese-language study began at 10 AM CST, while the English-language study started at 5 PM EST. To illustrate the diversity of our participants, we visualize their nationalities in Figure 7. This figure demonstrate that UNIMORAL captures a broad geographic range, reflecting a variety of cultural backgrounds. Participants in our data collection were able to access a separate webpage displaying their consolidated moral and cultural scores, allowing them to gain insights from the results. This also serves as an additional incentive for participation. Figure 10 provides a preview of this webpage. The entire pipeline for collecting UNIMORAL is visualized in Figure 8.

A.4 Calculation of the Moral and Cultural Scores from Participant Responses

The Moral Foundation Questionnaire (MFQ) (Kohlberg, 1963) is designed to measure moral values based on Moral Foundations Theory (MFT), which identifies five key moral dimensions: Care, Fairness, Loyalty, Authority, and Purity. A recent extension of this, the MFQ2 (Atari et al., 2023), introduces another dimension of Proportionality and add relevant questions to the original MFQ questionnaire. The MFQ and MFQ2 consists of two sections: one assessing the relevance of different moral concerns (e.g., “Whether or not someone suffered emotionally”) and another evaluating agreement with moral statements (e.g., “Compassion for those who are suffering is the most crucial virtue”). Participants respond using a Likert scale, typically ranging from 1 (not at all relevant/strongly disagree) to 5 (extremely relevant/strongly agree). To compute moral scores, responses for each moral foundation are averaged separately from the relevance and endorsement sections. For example, the score for the moral foundation of care is calculated by averaging the likert scores for questions 1, 7, 13, 19, 25, and 31. A higher score on a particular foundation indicates a stronger inclination toward that moral dimension. We use the MFQ2 in our study, thus dealing with 6 dimension score of morality.

The Values Survey Module (VSM) (Hofstede, 1994), developed by Geert Hofstede, measures cultural dimensions across societies, focusing on six key aspects:
<table>
  <tr>
    <th>English</th>
    <th>Chinese</th>
    <th>Russian</th>
  </tr>
  <tr>
    <td>Kevin, a police officer, has been assigned to arrest a suspect in a serious crime case. The suspect is his cousin. After reviewing the evidence, Kevin finds it to be circumstantial, leading him to question his cousin’s guilt. Meanwhile, the actual perpetrator remains at large. What course of action should Kevin take?</td>
    <td>警官凯文被派去逮捕一个严重犯罪案的嫌疑人嫌疑人是他的堂兄经过审查证据后，凯文发现证据是间接的，这让他质疑堂兄的罪行与此同时，实际的罪犯仍然在逃凯文应该采取什么行动？</td>
    <td>Кевин, полицейский, получил задание арестовать подозреваемого в серьезном преступном деле. Подозреваемый — его двоюродный брат. После рассмотрения доказательств, Кевин считает, что это косвенное, что заставляет его сомневаться в вине своего двоюродного брата. Тем временем, фактический преступник остается на свободе. Какой курс действий должен принять Кевин?</td>
  </tr>
  <tr>
    <td>Possible Actions
1. Arrest the cousin based on the available evidence, potentially sacrificing family ties and his cousin’s freedom, even if he may be innocent.
2. Withhold the arrest and further investigate, risking the actual perpetrator claiming more victims and damaging public trust in the police, but also preserving the possibility of his cousin’s innocence.</td>
    <td>1. 基于现有的证据逮捕表兄弟, 潜在地牺牲家庭关系和表兄弟的自由, 即使他可能无辜。
2. 风险是实际的罪犯声称更多受害者, 损害公众对警方的信任。</td>
    <td>1. Арестуйте двоюродного брата на основе имеющихся доказательств, потенциально по- жертвовав семейными связями и свободой его двоюродного брата, даже если он может быть невиновным.
2. Остановить арест и продолжить расследо- вание, рискуя тем, что фактический преступ- ник претендует на больше жертв и наносит ущерб доверию общественности к полиции, но также сохраняя возможность невиновно- сти его двоюродного брата.</td>
  </tr>
</table>

Table 3: Example scenario and possible action set from UNIMORAL with the English version along with the Chinese and Russian translated versions.

1. Power Distance (PDI) – The degree to which inequality in power and authority is accepted.
2. Individualism vs. Collectivism (IDV) – The extent to which people prioritize personal autonomy over group loyalty.
3. Masculinity vs. Femininity (MAS) – Whether a culture values competitiveness and achievement (masculine) or care and quality of life (feminine).
4. Uncertainty Avoidance (UAI) – The preference for structured, rule-based environments over ambiguity.
5. Long-Term Orientation vs. Short-Term Orientation (LTO) – The extent to which a society values perseverance and future rewards versus tradition and immediate gratification.
6. Indulgence vs. Restraint (IVR) – The level of emphasis on personal enjoyment and leisure versus strict social norms.

Each cultural dimension is measured using multiple questionnaire items, with responses typically rated on a five-point Likert scale. Scores for each dimension are calculated by averaging the responses associated with that dimension, then adjusting using Hofstede’s formula (Hofstede and Minkov, 2013). For instance, the score for PDI is calculated as \( PDI = 35 \times (m_{07} - m_{02}) + 25 \times (m_{20} - m_{23}) \), where \( m_x \) refers to the annotator’s response to question number \( x \). These scores provide insights into dominant cultural values and their influence on decision-making and social behavior.

A.5 UNIMORAL Statistics

Table 1 illustrates the language wise statistics for UNIMORAL.. As can be observed, each language contains a total of 1464 instances, making the total number of instances in UNIMORAL across the six languages as 8784. We also show one example scenario and possible action set generated using the Llama-3.1-70B Instruct model in Table 3. Translations for Chinese and Russian, obtained via the Seamless-m4t-v2 model, are also shown.

A.6 Prompts

This study involves the usage of prompts in two ways – 1) generating the data for human annotation, and 2) prompting the LLMs for evaluation of the data. All prompts that are selected for final use, whether in data generation or model evaluation, are finalized from an initial pool of possible prompts. We consider a number of candidate prompts, conduct initial experiment using them over a small subset of the English data and compare the results manually. We then select the best performing prompts and translate them to all the six languages required. We use chain of thought prompting for data generation while single prompts are used for evaluating LLMs. We list the prompts selected below. Anything in between square brackets is a placeholder, which is replaced with content before prompting any model.
Scenario generation Prompt 1:
You are a moral psychology expert. Given the following example scenario: [SEED SCENARIO], write its moral information, decision maker role, emotions elicited in the decision maker, consequence of the possible actions, and non moral and non emotional factors, such as legality, game rules, sacred values, culture, and social relations, involved in the decision making process.

Mention all these in no more than one line each. Just return the characteristics as a JSON file.

Scenario generation Prompt 2:
Given the following definition of a scenario:

A scenario is a detailed and structured description of a hypothetical situation designed to explore potential actions and outcomes. It includes several key elements. The Actor Role identifies the primary individual or group involved in the scenario. The Context provides the background and setting where the scenario takes place. Key Players are other significant individuals or groups that interact with the main actor. The Actor Objectives outline the goals, conflicts, and dilemmas faced by the actor within the scenario. Finally, the Action Question is a prompt that asks the actor to determine the best course of action based on the given information.

Write [NUM SCENARIOS] scenarios, in third person mode, similar to the following example scenario: [SEED SCENARIO]. The key characteristics of the scenario is described here: [PROMPT1 RESPONSE].

Write the scenarios in a paragraph format in no more than five lines, eliciting a similar moral dilemma such that each scenario contains the following characteristic: The [MOTIVATION] becomes the driving force of the decision making process. This sce-

nario should not be different from the past scenarios. Just return the scenarios as a JSON file with keys as scenatio number and value as the scenario.

Scenario generation Prompt 3:
Rewrite the following scenario so that it focuses only on the facts and ends with an open-ended question about what to do, removing any motivational details. Here is the scenario: [SCENARIO]

Action generation Prompt 1:
Consider the following scenario: [SCENARIO].

Deconstruct it to find out the immediate key stakeholders, list out their emotions, needs, biases in terms of pro and anti, and social influence given the scenario. Answer succinctly in a nested JSON format with the first level keys being the stakeholders and the second level keys are 'emotion', 'need', 'bias', and 'social influence'.

Action generation Prompt 2:
Give me the most apparent two actions that the main actor can take irrespective of them being morally correct or not based on the following scenario: [SCENARIO] and the following aspects of stakeholders: [PROMPT1 RESPONSE].

Give me one JSON file with the keys being action id and the values being the actions.

Action generation Prompt 3:
Rephrase these actions in a way that they look like possible options. Consider this scenario – [SCENARIO]; and add a sense of what they’ll lose to the action options without being definitive. For e.g. rephrase "Spend time searching for cure" to "Spend time searching for cure knowing that it might reduce time spent with wife during her last days". [ACTIONS]. Keep the rephrased response short. Return only a JSON file with keys being the serial number and the value being the rephrased ac-
AP – with persona:
Consider this scenario: [SCENARIO]; and the following possible actions: [ACTIONS].

Your job is to think like a person who introduces them as follows: [PERSONA], and choose one of the possible actions given the scenario. Strictly give your answer using the following format: "Selected action is <a or b>.". Do not write any explanation.

MTC – with moral values:
Consider the following definition of moral values:

Care - Prioritizing kindness, compassion, and the prevention of harm to others.
Equality - Ensuring that all individuals have the same rights, opportunities, and treatment.
Proportionality - Rewarding or punishing individuals in fair proportion to their actions.
Loyalty - Maintaining steadfast commitment and allegiance to ones group or community.
Authority - Respecting and upholding legitimate leadership, rules, and social order.
Purity - Valuing cleanliness, sanctity, and self-discipline in thoughts and actions.

Now, consider this scenario: [SCENARIO]; and the following possible actions: [ACTIONS]. Your job is to think like a person who values [MORAL VALUE 1] the most followed by [MORAL VALUE 2], [MORAL VALUE 3], [MORAL VALUE 4], [MORAL VALUE 5], [MORAL VALUE 6]; who selected to do the following action: [SELECTED ACTION].

Identify what type of action this is out of the following ['Deontological', 'Utilitarianism', 'Rights-based', 'Virtuous'].

Strictly give your answer using the following format: "Selected action is <'Deontological' or 'Utilitarianism' or 'Rights-based' or 'Virtuous'>.". Do not write any explanation.

FAA – with few shot examples:
Consider the following example scenario, the action selected by person A, and the contributing factor this action was influenced by.

Scenario: [FS SCENARIO]; Selection Action: [FS ACTION]; Action type: [FS CONTRIBUTING FACTOR]

Now, given the following scenario, and the action taken for the scenario by person A, your job is to identify the most important factor that contributed in the person’s decision making out of the following ['Emotions', 'Moral', 'Culture', 'Responsibilities', 'Relationships', 'Legality', 'Politeness', 'Sacred values'].

Strictly give your answer using the following format: "Selected action is <'Emotions', or 'Moral', or 'Culture', or 'Responsibilities', or 'Relationships', or 'Legality', or 'Politeness', or 'Sacred values'>.". Do not write any explanation.

CG:
Consider this scenario: [SCENARIO]; and the following selected action: [SELECTED ACTION].

Your job is to generate the consequence of this action, given the scenario in a concise manner. Be brief. Strictly give your answer using the following format: "Consequence of the action is "followed by the generation. Do not write any explanation.

A.7 Results for AP, MTC, FAA, and CG

The main paper highlights the key findings obtained from our analysis in a concise and graphical way. Here, we enumerate the results obtained in a Tabular way to illustrate all intermediate values obtained as well. Table 4, Table 5, and Table
<table>
  <tr>
    <th></th>
    <th>Arabic</th>
    <th>Chinese</th>
    <th>English</th>
    <th>Hindi</th>
    <th>Russian</th>
    <th>Spanish</th>
  </tr>
  <tr>
    <th rowspan="3">Personal</th>
    <td>Phi</td>
    <td>50.72</td>
    <td>59.74</td>
    <td>61.56</td>
    <td><b>64.40</b></td>
    <td>58.69</td>
    <td>63.45</td>
  </tr>
  <tr>
    <td>Llama</td>
    <td><b>61.80</b></td>
    <td>61.62</td>
    <td>66.05</td>
    <td><b>64.05</b></td>
    <td>61.92</td>
    <td><b>66.17</b></td>
  </tr>
  <tr>
    <td>R1</td>
    <td><b>69.41</b></td>
    <td>51.50</td>
    <td>50.72</td>
    <td>55.26</td>
    <td>55.26</td>
    <td>55.26</td>
  </tr>
  <tr>
    <th rowspan="3">Moral</th>
    <td>Phi</td>
    <td>51.14</td>
    <td>57.42</td>
    <td><b>66.38</b></td>
    <td>51.10</td>
    <td>59.02</td>
    <td>62.60</td>
  </tr>
  <tr>
    <td>Llama</td>
    <td>53.43</td>
    <td>61.64</td>
    <td>59.97</td>
    <td>58.06</td>
    <td><b>61.94</b></td>
    <td>65.20</td>
  </tr>
  <tr>
    <td>R1</td>
    <td>55.02</td>
    <td>51.57</td>
    <td>51.60</td>
    <td>50.27</td>
    <td>54.98</td>
    <td>55.09</td>
  </tr>
  <tr>
    <th rowspan="3">Culture</th>
    <td>Phi</td>
    <td>51.81</td>
    <td>55.11</td>
    <td><b>65.58</b></td>
    <td>53.56</td>
    <td>60.10</td>
    <td>60.08</td>
  </tr>
  <tr>
    <td>Llama</td>
    <td>50.57</td>
    <td><b>61.95</b></td>
    <td>59.00</td>
    <td>53.34</td>
    <td>61.66</td>
    <td>61.38</td>
  </tr>
  <tr>
    <td>R1</td>
    <td>52.51</td>
    <td>48.96</td>
    <td>47.06</td>
    <td>44.32</td>
    <td>52.31</td>
    <td>53.89</td>
  </tr>
  <tr>
    <th rowspan="3">Fewshot</th>
    <td>Phi</td>
    <td>55.66</td>
    <td>53.99</td>
    <td>62.65</td>
    <td>50.53</td>
    <td>57.39</td>
    <td>62.07</td>
  </tr>
  <tr>
    <td>Llama</td>
    <td>51.29</td>
    <td>57.60</td>
    <td>59.52</td>
    <td>55.73</td>
    <td>58.58</td>
    <td>65.11</td>
  </tr>
  <tr>
    <td>R1</td>
    <td>53.09</td>
    <td>53.16</td>
    <td>46.72</td>
    <td>52.07</td>
    <td>51.38</td>
    <td>48.37</td>
  </tr>
</table>

Table 4: Results for [I] Action Prediction. The numbers shown are weighted F1-scores where bold highlights best performance across language, and underline highlights best performance across model. Red color signifies performance below random. [Abbreviations – Phi: Phi-3.5-mini Instruct, Llama: Llama-3.1-8B-Instruct, R1: DeepSeek-R1-Distill-Llama-8B]

<table>
  <tr>
    <th></th>
    <th>Arabic</th>
    <th>Chinese</th>
    <th>English</th>
    <th>Hindi</th>
    <th>Russian</th>
    <th>Spanish</th>
  </tr>
  <tr>
    <th rowspan="3">Personal</th>
    <td>Phi</td>
    <td>39.00</td>
    <td>37.27</td>
    <td>46.37</td>
    <td>38.19</td>
    <td>38.41</td>
    <td>41.35</td>
  </tr>
  <tr>
    <td>Llama</td>
    <td>33.44</td>
    <td>29.86</td>
    <td>34.56</td>
    <td>33.35</td>
    <td>34.05</td>
    <td>40.30</td>
  </tr>
  <tr>
    <td>R1</td>
    <td>26.37</td>
    <td><b>16.33</b></td>
    <td>23.63</td>
    <td>34.77</td>
    <td>26.63</td>
    <td><b>13.82</b></td>
  </tr>
  <tr>
    <th rowspan="3">Moral</th>
    <td>Phi</td>
    <td>14.18</td>
    <td>36.82</td>
    <td>45.66</td>
    <td><b>13.65</b></td>
    <td>22.83</td>
    <td>17.10</td>
  </tr>
  <tr>
    <td>Llama</td>
    <td><b>21.09</b></td>
    <td>28.59</td>
    <td>45.99</td>
    <td>30.39</td>
    <td><b>50.85</b></td>
    <td><b>57.01</b></td>
  </tr>
  <tr>
    <td>R1</td>
    <td>40.72</td>
    <td>21.67</td>
    <td>25.23</td>
    <td>41.07</td>
    <td>45.09</td>
    <td>30.25</td>
  </tr>
  <tr>
    <th rowspan="3">Culture</th>
    <td>Phi</td>
    <td>16.25</td>
    <td>33.26</td>
    <td>37.34</td>
    <td><b>15.10</b></td>
    <td>30.24</td>
    <td>19.18</td>
  </tr>
  <tr>
    <td>Llama</td>
    <td>28.57</td>
    <td>29.41</td>
    <td>33.39</td>
    <td>17.42</td>
    <td>35.57</td>
    <td>54.92</td>
  </tr>
  <tr>
    <td>R1</td>
    <td>42.70</td>
    <td><b>23.24</b></td>
    <td>27.04</td>
    <td>39.40</td>
    <td>44.76</td>
    <td>23.09</td>
  </tr>
  <tr>
    <th rowspan="3">Fewshot</th>
    <td>Phi</td>
    <td><b>20.84</b></td>
    <td>34.49</td>
    <td><b>54.07</b></td>
    <td>22.54</td>
    <td>32.75</td>
    <td>37.26</td>
  </tr>
  <tr>
    <td>Llama</td>
    <td>38.62</td>
    <td>34.23</td>
    <td>29.56</td>
    <td>35.35</td>
    <td><b>23.84</b></td>
    <td>30.69</td>
  </tr>
  <tr>
    <td>R1</td>
    <td><b>53.82</b></td>
    <td>27.10</td>
    <td>42.96</td>
    <td><b>46.06</b></td>
    <td>44.67</td>
    <td>29.01</td>
  </tr>
</table>

Table 5: Results for [II] Moral Typology Classification. The numbers shown are weighted F1-scores where bold highlights best performance across language, and underline highlights best performance across model. Red color signifies performance below random. [Abbreviations – See Table 4]

<table>
  <tr>
    <th></th>
    <th>Arabic</th>
    <th>Chinese</th>
    <th>English</th>
    <th>Hindi</th>
    <th>Russian</th>
    <th>Spanish</th>
  </tr>
  <tr>
    <th rowspan="3">Personal</th>
    <td>Phi</td>
    <td>23.94</td>
    <td>26.81</td>
    <td>27.69</td>
    <td>25.86</td>
    <td>32.70</td>
    <td>34.59</td>
  </tr>
  <tr>
    <td>Llama</td>
    <td>32.48</td>
    <td>31.95</td>
    <td><b>35.11</b></td>
    <td>29.82</td>
    <td>35.55</td>
    <td><b>36.55</b></td>
  </tr>
  <tr>
    <td>R1</td>
    <td>25.89</td>
    <td>18.65</td>
    <td>27.15</td>
    <td>21.41</td>
    <td>20.32</td>
    <td>22.75</td>
  </tr>
  <tr>
    <th rowspan="3">Moral</th>
    <td>Phi</td>
    <td>19.26</td>
    <td>20.86</td>
    <td>23.32</td>
    <td>28.86</td>
    <td>28.64</td>
    <td>37.38</td>
  </tr>
  <tr>
    <td>Llama</td>
    <td>34.14</td>
    <td><b>33.82</b></td>
    <td>24.98</td>
    <td>19.46</td>
    <td>29.98</td>
    <td>21.23</td>
  </tr>
  <tr>
    <td>R1</td>
    <td>30.52</td>
    <td>17.69</td>
    <td>22.81</td>
    <td>21.86</td>
    <td>29.57</td>
    <td>26.37</td>
  </tr>
  <tr>
    <th rowspan="3">Culture</th>
    <td>Phi</td>
    <td>15.15</td>
    <td>26.12</td>
    <td>33.54</td>
    <td>26.04</td>
    <td>26.10</td>
    <td>29.16</td>
  </tr>
  <tr>
    <td>Llama</td>
    <td>6.30</td>
    <td>12.69</td>
    <td>13.31</td>
    <td><b>11.81</b></td>
    <td><b>11.54</b></td>
    <td><b>11.99</b></td>
  </tr>
  <tr>
    <td>R1</td>
    <td>21.77</td>
    <td>18.33</td>
    <td>26.24</td>
    <td>16.50</td>
    <td>25.75</td>
    <td>25.89</td>
  </tr>
  <tr>
    <th rowspan="3">Fewshot</th>
    <td>Phi</td>
    <td>29.01</td>
    <td>21.34</td>
    <td>30.58</td>
    <td>32.59</td>
    <td>32.30</td>
    <td>28.16</td>
  </tr>
  <tr>
    <td>Llama</td>
    <td>26.84</td>
    <td>26.23</td>
    <td>27.33</td>
    <td>15.68</td>
    <td><b>38.59</b></td>
    <td>25.31</td>
  </tr>
  <tr>
    <td>R1</td>
    <td><b>36.61</b></td>
    <td>14.82</td>
    <td>24.34</td>
    <td>33.36</td>
    <td>28.36</td>
    <td>19.68</td>
  </tr>
</table>

Table 6: Results for [III] Factor Attribution Analysis. The numbers shown are weighted F1-scores where bold highlights best performance across language, and underline highlights best performance across model. Red color signifies performance below random. [Abbreviations – See Table 4]

<table>
  <tr>
    <th></th>
    <th colspan="2">Phi</th>
    <th colspan="2">Llama</th>
    <th colspan="2">R1</th>
  </tr>
  <tr>
    <th></th>
    <th>B</th>
    <th>BS</th>
    <th>B</th>
    <th>BS</th>
    <th>B</th>
    <th>BS</th>
  </tr>
  <tr>
    <td>Arabic</td>
    <td>1.27</td>
    <td>3.68</td>
    <td><b>71.31</b></td>
    <td>1.20</td>
    <td>6.38</td>
    <td>69.84</td>
    <td>0.48</td>
    <td>2.79</td>
    <td>64.77</td>
  </tr>
  <tr>
    <td>Chinese</td>
    <td>0.06</td>
    <td>0.17</td>
    <td>64.84</td>
    <td>0.01</td>
    <td>0.06</td>
    <td><b>65.89</b></td>
    <td>0.00</td>
    <td>0.00</td>
    <td>62.99</td>
  </tr>
  <tr>
    <td>English</td>
    <td>3.29</td>
    <td>13.65</td>
    <td>86.48</td>
    <td>2.37</td>
    <td>19.08</td>
    <td><b>87.44</b></td>
    <td>2.42</td>
    <td>16.71</td>
    <td>87.07</td>
  </tr>
  <tr>
    <td>Hindi</td>
    <td>0.73</td>
    <td>2.99</td>
    <td>67.81</td>
    <td>2.08</td>
    <td>11.12</td>
    <td><b>70.78</b></td>
    <td>1.14</td>
    <td>7.37</td>
    <td>68.56</td>
  </tr>
  <tr>
    <td>Russian</td>
    <td>0.88</td>
    <td>2.71</td>
    <td><b>67.88</b></td>
    <td>0.95</td>
    <td>5.81</td>
    <td>67.07</td>
    <td>0.49</td>
    <td>4.20</td>
    <td>65.26</td>
  </tr>
  <tr>
    <td>Spanish</td>
    <td>1.87</td>
    <td>9.57</td>
    <td>68.69</td>
    <td>1.66</td>
    <td>15.60</td>
    <td><b>70.56</b></td>
    <td>1.51</td>
    <td>11.14</td>
    <td>69.92</td>
  </tr>
</table>

Table 7: Results for [IV] Consequence Generation. The numbers shown are BLEU (B), METEOR (M), and BERTScore (BS) where bold highlights best performance across language, and underline highlights best performance across model. Red color signifies performance below random. [Abbreviations – See Table 4]

<table>
  <tr>
    <th>Scenario</th>
    <th colspan="3">Selected Action</th>
    <th colspan="3">GT</th>
  </tr>
  <tr>
    <th></th>
    <th>Phi</th>
    <th>Llama</th>
    <th>R1</th>
    <th>Phi</th>
    <th>Llama</th>
    <th>R1</th>
  </tr>
  <tr>
    <td>Report the plagiarism to the university administration, potentially jeopardizing the student’s future and their ability to support their disabled family member.</td>
    <td>The student will be required to redo the thesis or face academic penalties, potentially jeopardizing their future and their ability to secure a better job and support their disabled family member.</td>
    <td>The student may face disciplinary action, potentially losing their degree and jeopardizing their ability to secure employment, which could negatively impact their ability to support their disabled family member.</td>
    <td>there would be potential harm to the student.</td>
    <td>the student facing academic disciplinary action, risking their job prospect</td>
    <td>the student will be required to redo the thesis or face academic penalties, potentially jeopardizing their future and their ability to secure a better job and support their disabled family member.</td>
    <td>The student may face disciplinary action, potentially losing their degree and jeopardizing their ability to secure employment, which could negatively impact their ability to support their disabled family member.</td>
  </tr>
</table>

Table 8: Sample predictions made by the model given the scenario. [Abbreviations – GT: Ground Truth, Phi: Phi-3.5-mini Instruct, Llama: Llama-3.1-8B-Instruct, R1: DeepSeek-R1-Distill-Llama-8B]

6 illustrates the results obtained for AP, MTC, and FAA, respectively, where we have highlighted the best performance obtained across models (rows) and languages (columns). Table 7 showcases the results obtained for CG. We see from Table 7 that we obtain high score for semantic similarity and a low score for syntactic similarity indicating that the generated consequences, may not follow a similar wordings than what is written by annotation participants, but they mean similar. We show an example generation and its corresponding ground truth in Table 8. As can be seen from the table, and manually observed in other cases as well, LLMs tend to generate longer text when compared with human annotators, and they try to include more context in the consequence as well. While this results in low score over syntactic metrics, it, in no way, means that the consequence generated are bad. BERTScore supports our claim.
A.7.1 Reproducibility Details
Experiments are conducted on 4 NVIDIA A100-SXM4-80GB GPUs using Hugging Face Transformers 4.43.3 (Wolf et al., 2020) and PyTorch 2.4.0 (Paszke et al., 2019) on a CUDA 12.4 environment. To ensure reproducibility, we set all random seeds in Python to be 42, including PyTorch and NumPy. We keep max generation length as 2000 tokens, rest all settings are default. Additionally, we plan to publicly release UNIMORAL upon the acceptance of this paper to support further research in moral reasoning and NLP.
Introduction
Welcome to the Study of Morals and Cultures! Get ready for an exciting exploration into your values! This study has two parts:
• Part One: You’ll encounter a series of intriguing scenarios, just like the example below, along with potential actions. Your mission? Choose the most fitting response based on your intuition! Be honest and answer imagining yourself in the scenario and what would you do.
• Part Two: After completing the scenarios, you’ll take a quiz designed to uncover your moral and cultural values. You’ll answer questions about your perspectives on various moral and cultural situations, revealing how these values influence your decision-making process.

Example Scenario
Heinz’s wife is dying from a particular type of cancer. There is a drug that might save her, but it is very expensive, and Heinz cannot afford it. The pharmacist who discovered the drug refuses to sell it for any less or to let Heinz pay later. Heinz is considering breaking into the pharmacy to steal the drug. What would you do in this scenario?

Why you are answering these questions? Morality is a complex cognitive trait in humans, influenced by environment, personal experiences, and life lessons. This study aims to analyze how individuals from diverse cultural backgrounds make moral judgments. Your responses will contribute to a holistic moral reasoning dataset, which will be the first comprehensive resource for advancing research in pluralistic moral reasoning within NLP.

Risks: You might be occasionally discomforted, offensive or distressing content.
Confidentiality: By participating in this research, you understand and agree that the researcher may be required to disclose your consent form, data, and other personally identifiable information as required by law, regulation, subpoena, or court order. Otherwise, your confidentiality will be maintained in the following manner:
To protect your identity, the researchers will take the following steps:
1. Each participant will be assigned a number
2. The researchers will record any data collected during the study by number, not by name
3. Any data files will be stored in a secured location accessed only by authorized researchers.

Consent: Your participation in this research is voluntary. You may discontinue participation at any time during the research activity.

I have read and understood the instructions.
[ ] Yes
[ ] No

(a) Introduction shown to the participants for their informed consent.

Scenario
A college student living with roommates faces a moral dilemma when their roommates, who have been treating them unfairly and excluding them from activities due to disliked their race, ask them to contribute to household chores without acknowledging or addressing the existing issue.

Possible Actions
(A) Refuse to contribute to household chores as a form of protest against the unfair treatment
(B) Continue contributing to household chores despite the unfair treatment to maintain a sense of responsibility and harmony in the living environment

<table>
  <tr>
    <th colspan="6">1. Out of the candidate actions, what do you think is the preferable thing to do in this scenario?</th>
  </tr>
  <tr>
    <td>(A)</td>
    <td>(B)</td>
  </tr>
  <tr>
    <th colspan="6">2. Why did you select the preferred action? Answer briefly.</th>
  </tr>
  <tr>
    <td colspan="6"></td>
  </tr>
  <tr>
    <th colspan="6">3. What will be the consequence of the selected preferred action? Answer briefly.</th>
  </tr>
  <tr>
    <td colspan="6"></td>
  </tr>
  <tr>
    <th colspan="6">4. How much does the selected preferred action satisfy these criteria?</th>
  </tr>
  <tr>
    <th>No Contribution</th>
    <th>Slight Contribution</th>
    <th>Moderate Contribution</th>
    <th>High Contribution</th>
    <th>Maximal Contribution</th>
  </tr>
  <tr>
    <td>Following rules</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Doing good to most people</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Respecting People's rights</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Acting with intent</td>
    <td></td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
</table>

(b) Sample annotation page the user see and has to fill.

Figure 9: Screenshots from our annotation platform developed using Potato.
Morality and Culture Quiz Results

Based on your responses to the moral and cultural questionnaires, we have completed the following results. These insights reveal the influence of different moral and cultural values in your decision-making process.

Your Moral Compass Quiz Results
Care: High scores show deep empathy and a drive to protect others. Lower scores suggest you may take a more pragmatic approach, balancing personal needs with others welfare.
Equality: Scoring high here indicates a strong commitment to fairness and justice, whereas a lower score may mean you prioritize flexibility over strict equity in social situations.
Proportionality: If you scored high, you likely value balanced outcomes, ensuring that rewards and consequences match the actions taken. A lower score suggests you may be more concerned about outcomes independent of intent.
Loyalty: A high score reflects your dedication to family, friends, and community, while a lower score could indicate more independence in choosing connections over traditional ties.
Authority: High scores respect established norms and structures, valuing order. A low score might mean you are more open to questioning or redefining traditional rules.
Purity: High scores reveal a strong alignment with personal ideals and integrity, while lower scores may suggest a more flexible or grounded perspective on ethical considerations.

Your Cultural Values Quiz Results
Power Distance (PD): A high score indicates you favor structured hierarchies and clear authority, valuing stability. A low score suggests you embrace equality and prefer collaborative environments with shared power.
Individualism (IDV): If you scored high, you prioritize personal freedom and independence. Lower scores reflect a strong inclination towards teamwork and valuing group harmony over individual pursuits.
Masculinity (MAS): High scores reveal a focus on assertiveness and competitive drive, while lower scores highlight a preference for relationships and emotional expression.
Uncertainty Avoidance (UAI): High-score indicates a strong need for structure and predictability, while a lower score suggests comfort with ambiguity and an adaptable approach to the unknown.
Long Term Orientation (LTO): If you scored high, you tend to focus on future planning and perseverance, while a lower score may indicate a preference for immediate results and living in the moment.
Indulgence vs Restraint (IMR): High scores suggest a tendency to embrace life's pleasures and enjoy freedom, whereas lower scores indicate a preference for self-discipline and restraint.

Figure 10: Moral and Cultural score results shown to the user, as aggregated from the MFQ2 and VSM of the participant.

Reviewer paper summaries: This paper introduces UniMoral, a unified multilingual dataset integrating psychologically and socially derived moral dilemmas to enhance computational moral reasoning research, demonstrating its utility through LLM benchmarks and highlighting the need for more specialized approaches to improve moral reasoning in AI.

The authors propose UNI-MORAL, a dataset that combines morally relevant dilemmas derived from psychology and social media. This dataset is annotated with labels for action choices, ethical principles,
contributing factors, and consequences, as well as annotators' moral and cultural profiles. Their findings
indicate that while implicit moral contexts improve the moral reasoning abilities of large language models
(LLMs), there is still a need for more specialized methods to further enhance moral reasoning in these
models.
Summary of strengths: - The problem addressed in this paper is very important, and I believe the dataset proposed by the authors is highly valuable.
- The writing is very clear and easy to understand.

The paper proposes a valuable dataset to computationally model complex reasoning in NLP with comprehensive research questions well-grounded in literature. The paper is easy to follow and well-structured.
Summary of weaknesses: - Overall, I do not have major concerns about this paper, though it may lack comparisons with related literature [1].

- Additionally, could you provide information on the language proficiency of the human annotators? For example, are they fluent in specific languages?


[1] Huang, Yue, Zhengqing Yuan, Yujun Zhou, Kehan Guo, Xiangqi Wang, Haomin Zhuang, Weixiang Sun et al. "Social Science Meets LLMs: How Reliable Are Large Language Models in Social Simulations?." arXiv preprint arXiv:2410.23426 (2024).

The connection between the motivation and the methodology needs to be strengthened. Furthermore, the paper contains strong statements that need more experimental support. Some choices and arguments presented need clarification and further explanation.

- Lines 73-74: That’s a tricky main question and claim you made “to probe how moral frameworks vary across populations”. The first question that has to be answered and checked is whether LLMs perform differently in reasoning (general) for different languages. Hence, different results may arise because of different capabilities of the model across the languages, rather than cultural (language?) differences. How can you determine if these results stem from differences in LLM performance across languages or from cultural differences?
- Lines 78-96: The questions are interesting, but the connection to the motivation and literature needs to be sharpened. 
- Lines 107-110: The first contribution is unclear to me. Are these stages collected and merged
from the literature? This description is not concrete enough for me.
- Lines 185-187: This is debatable. Kohlberg, Haidt, and others do not (as far as I know) propose sequential steps to arrive at an appropriate action. Kohlber focused on moral development, while Haidt on the intuitions behind a moral decision. Both are not step-by-step. I would challenge this affirmation. One of the challenges here is that indeed it is far from trivial to operationalize moral reasoning in concrete algorithmic procedure. A few more sentences to motivate your choice and how and why it differs from other approaches are appreciated.
- Lines 210-217: It seems like you only vaguely based the pipeline on theory. You need to strengthen the grounding of the approach to the literature and the motivation.
- Lines 252-257: Some big assumptions were made here. It would be good to expand a bit more and try to explain what comes from where and such.
- Lines 258-262: Have the scenarios and actions generated by LLMs been evaluated by humans? What criteria were used to assess the generated data from Llama? I would appreciate an explanation and clarification on this step.
- Lines 281-284: Have you evaluated the moral dilemmas generated by Llama on Reddit by humans? What criteria were used? More explanation is needed.
- Line 562: I would appreciate a few words to explain why only BERTScore is used instead of other metrics, like BLEU and ROUGE, for evaluating the similarity between the generated text and the ground truth.
- Lines 586-593: The differences in results across languages can be attributed to how effectively the model understands each language. Can you share your thoughts on how you ensure that these differences in understanding do not impact the results?
- Lines 603-609: Can you reflect on why "the performance is not significantly different from chance"? What is your hypothesis on whether fine-tuning the model with your data would mitigate the poor performance? If you believe it would, I suggest using a less definitive word here, as you are only evaluating your data with a generalized LLM.
- Lines 620-624: I would imagine that if you tested your hypothesis with only a few-shot learning
where you basically only provide models with a few examples, it may not be enough to support a
strong argument that “LLMs are not good enough for moral reasoning.”
- In your abstract, you mentioned that “there remains a critical need for increasingly specialized
approaches to further advance moral reasoning in these models.” I would appreciate a few sentences outlining potential future approaches that can be utilized to model moral reasoning.
Comments, suggestions and typos: See weaknesses

- Line 527: To prevent redundancy, you can specify your evaluation metrics in the experimental setup instead of repeating them for each method.
Overall assessment scores: 4

3.5
Metareview score: 4
Metareview: This paper introduces a dataset, UniMoral, for moral reasoning, this dataset proposes a framework for implementing moral reasoning and covers various languages and cultures. The proposed UniMoral is psychologically grounded and contain rich annotation schemas. 

The discussion between reviewers and the authors clarify the main concerns raised by reviewers and indicate a clear acceptance.
Summary of reasons to publish: 1. A moral reasoning framework and dataset designed for the task of moral reasoning, which remains a significant challenge for LLMs.
2.The introduced datasets cover multiple languages and cultures, bridging the gap left by previous datasets that were annotated only in English and western cultures.