Title: Unveiling the Bias Impact on Symmetric Moral Consistency of Large Language Models
Venue: NeurIPS 2024
Accepted: yes
Keywords: Large Language Model

Moral Consistency

Evaluation

Ethics
Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities, surpassing human experts in various benchmark tests and playing a vital role in various industry sectors. Despite their effectiveness, a notable drawback of LLMs is their inconsistent moral behavior, which raises ethical concerns. This work delves into symmetric moral consistency in large language models and demonstrates that modern LLMs lack sufficient consistency ability in moral scenarios. Our extensive investigation of twelve popular LLMs reveals that their assessed consistency scores are influenced by position bias and selection bias rather than their intrinsic abilities. We propose a new framework tSMC, which gauges the effects of these biases and effectively mitigates the bias impact based on the Kullback–Leibler divergence to pinpoint LLMs' mitigated Symmetric Moral Consistency. We find that the ability of LLMs to maintain consistency varies across different moral scenarios. Specifically, LLMs show more consistency in scenarios with clear moral answers compared to those where no choice is morally perfect. The average consistency score of 12 LLMs ranges from $60.7\%$ in high-ambiguity moral scenarios to $84.8\%$ in low-ambiguity moral scenarios.

Unveiling the Bias Impact on Symmetric Moral Consistency of Large Language Models

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities, surpassing human experts in various benchmark tests and playing a vital role in various industry sectors. Despite their effectiveness, a notable drawback of LLMs is their inconsistent moral behavior, which raises ethical concerns. This work delves into symmetric moral consistency in large language models and demonstrates that modern LLMs lack sufficient consistency ability in moral scenarios. Our extensive investigation of twelve popular LLMs reveals that their assessed consistency scores are influenced by position bias and selection bias rather than their intrinsic abilities. We propose a new framework tSMC , which gauges the effects of these biases and effectively mitigates the bias impact based on the Kullback-Leibler divergence to pinpoint LLMs' mi t igated S ymmetric M oral C onsistency. We find that the ability of LLMs to maintain consistency varies across different moral scenarios. Specifically, LLMs show more consistency in scenarios with clear moral answers compared to those where no choice is morally perfect. The average consistency score of 12 LLMs ranges from 60 . 7% in high-ambiguity moral scenarios to 84 . 8% in low-ambiguity moral scenarios.

1 Introduction

Large language models (LLMs) are gaining popularity in society due to their exceptional ability to perform various downstream tasks efficiently and effectively. Some models have surpassed human experts in benchmark tests [1, 2]. Despite their impressive performance, previous research has raised concerns about their responses in moral scenarios [3, 4], such as moral dilemmas [5] where two or more conflicting moral imperatives but none of which overrides the other. Research has shown potential risks with LLMs when supporting unethical or harmful behavior, which may lead users to engage in harmful actions they would not have otherwise taken [6, 7]. Additionally, researchers have expressed concerns about the potential confusion and uncertainty that LLMs could cause [8], which could hurt users' trust. Given these considerations, it is essential to accurately assess the behavior of LLMs in ethical scenarios to ensure the ethical development and positive impact of LLMs and mitigate their potential harm to humans.

Symmetric consistency, a type of logical consistency [9], implies that for a model M and options x and y of a problem, M ( x, y ) = M ( y, x ) , ensuring invariance to input text swaps. This characteristic is

crucial in moral scenarios, as input order can easily change, and different answers could reflect distinct moral preferences [10]. Inconsistent moral guidance from LLMs can influence users' decisions, leading to unforeseen outcomes [7]. Previous work has revealed the semantic consistency of LLMs when applied to ethical situations and has highlighted the inadequacies of LLMs in symmetric moral consistency [11]. Symmetric moral consistency is more easily identified in structure and commonly encountered in practice. Our research in the following sections shows that LLMs commonly exhibit a significant impact of both position bias [12] and selection bias [13] on symmetric moral consistency, irrespective of the model type or parameter size. This suggests that previous work may not accurately capture the true consistency of these models [9, 14].

1.1 Motivation and Problem: a Case Study of GPT-3.5-turbo

Definition Symmetric moral consistency in LLMs refers to their capacity to select identical responses to ethical-related questions involving two potential actions, even after interchanging specific components and rephrasing the wording. The mitigated symmetric moral consistency discussed in this paper refers to the symmetric moral consistency measured after mitigating the position bias and selection bias of LLMs. This attribute is crucial for ensuring that LLMs exhibit reliable and unbiased moral reasoning ability. LLMs can better support applications in ethical decision-making and policy formulation by maintaining consistent moral judgments across varied formulations of the same moral scenario.

Metric Assume that e is one prompt format and e N is a perturbed version (e.g., swap the option ID of e ); we expect model M to generate the same predictions for e and e N . The consistency score τ on dataset E can be observed as follows:

We adopt the same metric as Jang et al. [9, 14] to investigate LLMs' symmetric moral consistency. In particular, for tasks with option IDs, we assess whether the answer IDs in the model output remain identical after swapping the text. For tasks involving only option contents, we examine whether the model's responses are uniform across different settings, ensuring that the same option is selected in each experiment. In examining a moral scenario, we evaluate the symmetric moral consistency of LLMs through three methods: Context Swap (CS), Option Swap (OS), and Full Swap (FS), which involve swapping context, option ID, and the entire sentence .

Figure 1 illustrates the prompt template utilized for assessing GPT-3.5-turbo ; additional templates for diverse models and tasks are provided in Appendix A. We conduct experiments to investigate potential biases affecting the symmetric moral consistency of GPT-3.5-turbo . Weevaluate the three distinct experimental settings by measuring their average consistency scores (Avg) and standard deviations (Std). By assigning option IDs as AB and CD respectively, we try to assess whether GPT-3.5-turbo exhibits a discernible preference for specific IDs. Subsequently, by eliminating the option IDs and prompting the model to repeat preferred options, we aim to mitigate the influence of selection bias. Furthermore, we observe whether the consistency scores and deviations fluctuate throughout conventional few-shot and Chainof-Thought (CoT) experiments [15, 16]. Our goal is to assess the consistency levels of these models

Table 1: Comparison of GPT-3.5-turbo based on standard deviation (Std) and average symmetric consistency metric value (Avg). We find that biases result in significant deviations in the performance of LLMs, and current techniques can mitigate but not fully resolve these issues.

in high-ambiguity and low-ambiguity moral scenarios to highlight the potential negative impacts of current models.

As shown in Table 1, the results from the AB and CD groups reveal a notable dissimilarity. Specifically, the CD group demonstrates markedly lower deviation levels across both scenarios, with

Figure 1: Input format templates for LLMs . Black text represents the template input, including system and user prompts. Red text denotes the option ID and answer. Blue text contains the question and its associated potential actions, which vary for each scenario. The content and position of colored text will change depending on the experimental settings. Option IDs may also vary between C and D or other choices.

superior performance in high-ambiguity moral scenarios. Removing the IDs leads to a discernible enhancement in the model's consistency score. However, the CD group's consistency score in lowambiguity moral scenarios is unexpectedly inferior to that of the AB group, contrary to our initial expectations. Remarkably, both few-shot and CoT experiments substantially improve the model's average consistency scores while reducing deviation. From these experimental findings, it is evident that biases exert a negative influence on the symmetric moral consistency of GPT-3.5-turbo , particularly apparent in the prevalent AB single-choice question format, where biases and their consequences are magnified. We also measure LLMs' symmetric consistency on the MMLU high_school_us_history subject [17], which similarly reflects fluctuating performance under various experimental settings. This observation suggests that our findings can generalize to a broader range of scenarios. Detailed results can be found in Appendix D.

1.2 Our Contribution

A. I refuse to assist in suicide.

B. I assist in suicide.

Question: Your mother is terminally ill, in constant pain, and wishes to end her life. She asks you to

help to assist her in suicide.

Which of the following would you do? Respond solely with A or B.

A. I assist in suicide.

B. I refuse to assist in suicide.

B. I refuse to assist in suicide.

A. I assist in suicide.

B. I assist in suicide.

A. I refuse to assist in suicide.

Change context's position (CS)

Answer: A

Answer: B

Consistent?

YES?

Change option iD's position (OS)

Change entire sentence's position (FS)

Answer: B

Consistent?

NO! Position Bias

Answer: A

Consistent?

NO! Selection Bias

(a) Evaluation Diagram

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Figure 2 illustrates the complete evaluation process and outcomes for each model under assessment. The graph on the left represents our evaluation procedure, where we divide the experiment into

three parts and swap different prompt sections in each setting. We leverage the MoralChoice dataset [10], comprising scenarios with varying levels of ambiguity: low-ambiguity moral scenarios with clear preferences and high-ambiguity moral scenarios lacking definitive answers. In high-ambiguity moral scenarios, where neither answer is correct, it is crucial to ensure that LLMs exhibit consistent behavior across various prompt designs while encountering the exact scenarios. We aim to observe consistent results across different settings, indicating a stable preference for LLMs. On the right side, the graphs display the consistency scores of LLMs and the fluctuations in each condition compared to the standard assessment. The diverse results suggest inadequate stability in the consistency abilities of LLMs.

Our contributions can be summarized as follows:

To the best of our knowledge, we are the first to unveil the bias impact on LLMs' symmetric moral consistency. We propose a new framework to assess the impact of position bias and selection bias on LLMs' symmetric moral consistency under three settings (Context Swap, Option Swap, and Full Swap) and then evaluate the symmetric moral consistency after mitigating the bias impact based on the Kullback-Leibler divergence. Here, we refer to the framework as tSMC (mitigated Symmetric Moral Consistency).

We conduct extensive experiments on 12 LLMs, including 10 open-source and 2 proprietary closed-source models, and identify the presence of both position bias and selection bias in various LLMs, contributing to inaccuracies in measured consistency. These experiment results highlight the effectiveness and significance of our framework tSMC that could assess LLM's mitigated symmetric moral consistency. The key findings are:

Position bias and selection bias significantly impact the consistency performance of LLMs, leading to substantial fluctuations in measurement results across CS, OS, and FS settings. For instance, Llama-2-13b exhibits an almost 80% performance gap between OS and FS settings in high-ambiguity moral scenarios, illustrating the ineffectiveness of directly measuring symmetric consistency.

LLMs exhibit greater consistency in low-ambiguity moral scenarios than in highambiguity moral scenarios. Interestingly, the consistency performance of LLMs shows a low correlation with model parameter size. For example, Llama-2-7b has better consistency performance than Llama-2-70b , as shown in Figure 4a.

Confidence scores reflect the model's certainty in its responses, and LLMs exhibit higher confidence scores in low-ambiguity moral scenarios than in high-ambiguity moral scenarios. Surprisingly, though Phi-2 exhibits high consistency performance, it shows notably low confidence distributions in both moral scenarios.

2 Related Work

Morality and Ethics in LLMs There is a growing interest in evaluating the ethical competencies of LLMs [18, 19]. MoCa [4] evaluated whether LLMs make casual and moral judgments that align with humans. Scherrer et al. [10] evaluated moral beliefs encoded in LLMs and observed that specific models exhibit similar preferences. Tanmay et al. [5] measured the moral reasoning ability of LLMs using the Defining Issues Test. Abdulha et al. [20] and Fraser et al. [21] assessed LLMs with moral questionnaires based on Moral Theories [22, 23]. We evaluate the symmetric consistency of LLMs in moral scenarios and reveal the shortcomings of LLMs in this task.

Consistency in LLMs Consistency refers to the expectation of receiving identical answers for the same question presented in different formats. Elazar et al. [24] evaluated the consistency of PLMs, such as Bert [25]. Jang et al. [9, 14] assessed several forms of consistency in LLMs, such as ChatGPT, including semantic and logical consistency. Most research evaluated symmetric consistency in Natural Language Inference (NLI) tasks [26, 27], but these experiments cannot reflect the ability of LLMs in real scenarios. Bonagiri et al. [11] assessed the semantic moral consistency of LLMs and introduced an entropy measure based on the Rule of Thumbs (RoT). Jang et al. [9, 14] investigated a range of consistency measures, while our research specifically examines symmetric consistency in moral scenarios. Although their assessment of symmetric consistency corresponds to our Context Swap experimental design, we extend the analysis by introducing Option Swap and Full Swap experiments. These additional experiments aim to address the potential influences of position and

selection biases on LLMs. To the best of our knowledge, we are the first to evaluate the symmetric moral consistency of LLMs.

Value Alignment in LLMs The advanced capabilities of LLMs require significant efforts to align their behavior with human values [2, 15, 28, 29, 30, 31, 32]. Nie et al. [33] employed multi-step inference techniques to improve human approval ratings, and Jiang et al. [34] pretrained LLMs to anticipate real human reactions to ethical inquiries. Bai et al. [31] directly aligned LLMs' preferences with predefined rules to eliminate the requirement for human-labeled data. Unlike previous work, our paper evaluates symmetric moral consistency in LLMs rather than fine-tuning LLMs to align with human preferences.

Bias in LLMs In this paper, bias refers to LLMs' systematic error arising from the transformer architecture or other factors instead of social bias [35, 36, 37, 38]. Zhao et al. [39] found that GPT-3's responsiveness to in-context examples and task instructions can result in biased responses. Li et al. [12] demonstrated that LLM evaluators prefer either the first or second answer, irrespective of the content, indicating position bias can impact the model's robustness. Zheng et al. [13] proposed that LLMs exhibit selection bias over position bias, which means models tend to choose answers based on some specific option IDs. Our study reveals that both position bias and selection bias affect LLMs' symmetric moral consistency.

3 Our Framework

Bias Impact under Three Settings Due to the position bias, LLMs prefer the first or last option when making choices regardless of the content. As shown in Figure 2, this inclination improves the consistency scores of LLMs in the Option Swap setting but decreases them in the Context Swap and Full Swap settings. On the other hand, selection bias causes models to favor choosing a particular option ID, resulting in an improved consistency score in the Full Swap setting but hindering performance in the Context Swap and Option Swap settings. For instance, we observe that Llama-2-13b tends to choose option B in all settings with high-ambiguity moral scenarios. If we only swap the option ID or context, Llama-2-13b still chooses option B, significantly decreasing the assessed consistency values, as shown in Table 2. This results in a substantial performance gap in the consistency score between the Context Swap and the other two settings. Since the position and selection biases concurrently affect LLMs, it is crucial to quantify the extent of their influence and unveil the symmetric moral consistency of LLMs after mitigating such an impact.

Mitigated Symmetric Moral Consistency Score Because the probability distributions of LLMs' consistency performance vary across settings, we leverage the Kullback-Leibler divergence [40] to illustrate how biases impact LLMs. We derive consistency values across different factors by examining the values obtained in various experimental setups. After normalizing these values for a single model, we evaluate the divergence of position bias using Option Swap (OS) as the reference distribution and the divergence of selection bias using Full Swap (FS) as the reference distribution. The closer the value of D pos or D selec is to 0, the smaller the impact of the bias, and vice versa. Each model is tested across settings S = { CS, OS, FS } , and the above process can be formulated as:

where D pos in Equation 2 stands for the relative position bias and D selec in Equation 3 stands for the relative selection bias . The closer the value of D pos or D selec is to 0, the smaller the impact of the bias, and vice versa. P s represents the probability that a model makes a morally consistent choice in the setting s , and it can be calculated by metric τ . Similarly, P OS and P FS represent the probability in the Option Swap and Full Swap settings. Based on the relative biases, we can assess how much these biases affect LLMs' performance and calculate the mitigated symmetric moral consistency scores by:

where C mitig denotes the mitigated symmetric moral consistency of LLM, τ s denotes the consistency measured in the setting s . D total denotes the sum of the effects of position bias D pos and selection bias D selec under different settings, and it is derived from the analysis of bias impact under three settings. α is a hyper-parameter and we set α to 0 . 1 in our experiment.

Dataset We use the MoralChoice Dataset [10] to evaluate the symmetric moral consistency of LLMs. This dataset is initially designed to extract moral beliefs encoded in LLMs. It contains 680 high-ambiguity moral scenarios questions and 687 low-ambiguity moral scenarios questions. Each question offers two action s, which are options like ' I refuse to assist in suicide ' and ' I assist in suicide .' For the low-ambiguity moral scenarios, there will be an action option consistent with human values and common sense. For the high-ambiguity moral scenarios, neither action is preferred. Examples selected from this dataset are illustrated in Figure 2 and Figure 8.

Models Our analysis covers twelve decoder-only transformer models, comprising ten open-source and two commercial models. These models include Phi-2 , Gemma-2/7B , Llama-2-7/13/70B , Llama-3-8/70B , Mistral-7B , and Mixtral-8x7B . Except for Phi-2 , all models have undergone instruction tuning [41]. For commercial models, we evaluate GPT-3.5-turbo and Gemini-1.0-pro through API services. The link of each model can be found in Appendix E.

Evalution We follow the mainstream LLM evaluation frameworks like the HuggingFace LLM Leaderboard and the original MMLU implementation [17]. Specifically, we evaluate the likelihood of option ID tokens presented in the query for open-source LLMs, selecting the option with the highest probability as the response on A100 80G GPUs. We set the decoding temperature to 0 for commercial LLMs and assess their responses accordingly [42].

3.1 Consistency Scores and Fluctuations of LLMs

Table 2 presents the consistency scores of LLMs and the corresponding variations compared to the standard experiment setup in low-ambiguity and high-ambiguity moral scenarios separately. The results are in Appendix B when option IDs are CD. LLMs demonstrate greater consistency in moral reasoning in low-ambiguity situations compared to high-ambiguity ones, a finding aligned with the results of Scherrer et al. [10], where 'better' refers specifically to consistency rather than moral superiority. This is because, in low-ambiguity moral scenarios, there are action options that align with human values and common sense. However, both actions exhibit notable deficiencies in high-ambiguity moral scenarios, making it challenging for LLMs to make morally correct choices.

Furthermore, examining the fluctuations in consistency scores indicates that LLMs are significantly impacted by biases, which distort their symmetric moral consistency scores. This fluctuation is particularly pronounced in high-ambiguity situations, suggesting that LLMs are more susceptible to biases and tend to select specific answers when unsure about their choices, regardless of their actual beliefs. For example, Mistral-7B demonstrates minimal fluctuation in low-ambiguity moral scenarios but considerably higher fluctuation in high-ambiguity moral scenarios. This variability could be due to the uncertainty that LLMs encounter in these circumstances, causing them to possibly choose random answers depending on the order of input or the specific option ID, as evidenced by the noticeable decrease in consistency scores. Notably, larger models do not necessarily perform better in this context. For instance, while Llama-2-7b and Llama-2-70b are trained on the same data, the 7b model demonstrates superior symmetric moral consistency scores in both scenarios. In contrast, Llama-2-70b exhibits markedly low consistency scores in low-ambiguity moral scenarios but significantly high consistency scores in high-ambiguity moral scenarios when the entire sentence is swapped rather than under the standard experimental conditions. This suggests a strong selection bias toward the swapped sentence condition. Additionally, despite Llama-3-8b being trained on seven times more data than Llama-2-7b , it does not exhibit improved performance but instead encounters more pronounced fluctuation issues. Furthermore, Mixtral-8x7B and Gemini-1.0-pro exhibit minimal fluctuations in both scenarios.

3.2 LLMs' Biases in Different Scenarios

In this subsection, we measure and analyze the relative position bias D pos and the relative selection bias D selec of various LLMs. Figure 3 shows the measured results with AB as option IDs; the corresponding results for CD are available in Appendix B. First, the most intuitive manifestation is that the bias of LLMs is more concentrated in low-ambiguity moral scenarios and more dispersed in high-ambiguity moral scenarios. This indicates that in cases with clear tendencies towards answers, LLMs rarely lose their level of moral alignment due to bias. In particular, Mixtral-8x7B , Gemini-1.0-pro , and the Llama3 series show almost zero bias in low-ambiguity moral scenarios. This suggests that the credibility of the consistency scores exhibited by these models is very high. We set the threshold for both selection and position biases at 0.1. When models fall within the gray area delineated by the two dotted lines in Figure 3, we consider there to be no significant effect on the model's consistency score. Conversely, if models lie outside this gray area, we regard the biases significantly impacting the model's consistency score.

Nevertheless, in high-ambiguity moral scenarios, some models that perform well even in lowambiguity moral scenarios show higher bias, such as Llama-3-8b and Mistral-7B , whose biases in high-ambiguity moral scenarios are much higher than those in low-ambiguity moral scenarios. It is worth noting that the bias of some models is consistently high, such as Gemma-2b , Llama-2-13b , and Llama-2-70b , where their bias in high-ambiguity moral scenarios has reached the maximum value. Their bias in low-ambiguity moral scenarios is also beyond the threshold we set. This indicates that the answers of these models largely fail to reflect their level of consistency, and they choose answers from a specific row or option ID. Furthermore, it can be seen that models showing low-level bias in high-ambiguity moral scenarios (in the gray area we defined), such as Phi-2 , Mixtral-8x7B , and Gemini-1.0-pro , almost show no bias in low-ambiguity moral scenarios. This means that their assessed consistency scores accurately reflect their moral consistency. More importantly, these models exhibit high moral consistency and adhere to high ethical standards.

Moreover, Llama-2-13b and Llama-2-70b exhibit both high and similar position bias and selection bias values simultaneously. Sharing the same training data, they consistently demonstrate biases in both scenarios. This suggests that these biases might be inherent to the pre-training data or post-training methods and could potentially be mitigated through specific techniques, such as pre-training on more diverse datasets or instruction tuning to refine the models' responses based on targeted feedback and bias reduction strategies. However, the precise reasons for these similar biases remain unclear, highlighting the need for future in-depth investigation.

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3.3 LLMs' Mitigated Symmetric Moral Consistency

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After applying Equation 4 to the observed data, we calculate the mitigated symmetric moral consistency of LLMs, with results depicted in Figure 4a. Most models demonstrate robust consistency in low-ambiguity moral scenarios, indicating the alignment with human values and common sense. However, their consistency diminishes in high-ambiguity moral scenarios, suggesting that existing LLMs struggle to maintain consistent viewpoints when faced with morally ambiguous situations. This inconsistency raises concerns about the models' ability to uphold stable beliefs, potentially affecting user perception and underscoring the need for further research. Figure 4b and Figure 15 illustrate the mitigation effectiveness of the tSMC framework under different α settings in high- and low-ambiguity moral scenarios, respectively, compared to Context Swap. Notably, models such as Gemma 2/7b and Llama-2-7/13/70b exhibit low consistency scores even when a favored answer is present. The similar architectures and training data shared within model families suggest that specific techniques and structures can significantly influence their moral consistency.

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3.4 LLMs' Confidence Score

By evaluating the probability of actions in open-source LLMs and selecting the most probable one, we can determine the confidence levels of the chosen option ID within the models. Figure 5 and Figure 6 illustrate confidence distribution in different scenarios for open-source LLMs with option IDs AB.

In low-ambiguity moral scenarios, LLMs show a strong sense of certainty in their outcomes, as evidenced by a distribution that leans heavily towards 1 and is tightly concentrated. Conversely, when faced with high ambiguity, the models exhibit uncertainty, with probabilities clustering around 0.6, suggesting a lack of confidence in their choices (the minimum value for all models is 0.5, as we solely consider the winning option). Specifically, Llama-3-8b and Mistral-7B demonstrate uncertainty when confronted with ambiguous options but exhibit confidence when a clear preference exists. In contrast, Gemma-2b and Mixtral-8x7B consistently exhibit high confidence irrespective of the scenarios.

We observe that Phi-2 shows a notably low confidence in high-ambiguity moral scenarios. Surprisingly, this tendency persists in low-ambiguity moral scenarios and is close to a Gaussian distribution. As shown in Table 2 from prior experiments, Phi-2 consistently yields high consistency scores under different experimental settings. This indicates that while Phi-2 is adept at evaluating moral consistency, unlike Mixtral-8x7B , it seems hesitant in its judgments.

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phi-2

(b) High-Ambiguity Moral Scenarios

Figure 7 illustrates the correlation between moral consistency and confidence scores. These results demonstrate that confidence and consistency scores measure distinct aspects of model performance. Confidence scores reflect the model's certainty in its responses, while consistency scores indicate the model's ability to provide coherent judgments across scenarios. Figures 13 and 14 in Appendix B present the confidence scores of open-source models when option IDs are C and D. The models' confidence distributions remain nearly identical when A and B are employed as option IDs, with marginally higher confidence scores observed in the A-B scenario. This suggests a slightly stronger preference for models between options A and B, although it does not significantly impact the overall distribution.

4 Conclusion

In this paper, we have proposed a simple yet effective assessment framework tSMC to uncover the impact of position and selection biases on LLMs' symmetric moral consistency and assess the mitigated symmetric moral consistency of LLMs. Our study has revealed that most models exhibit higher consistency scores in low-ambiguity moral scenarios where preferences are clear, as opposed to high-ambiguity moral scenarios. This indicates that LLMs may not consistently adhere to moral principles, particularly in challenging situations such as moral dilemmas. These findings are also reflected in their confidence levels. Furthermore, specific models struggle to maintain consistency concerning biases, leading to inconsistent decision-making across different contexts, highlighting the need for further research to address these issues.

While our study provides valuable insights, there are certain limitations to acknowledge. Our assessment primarily utilizes a Multiple Choice Question Answering (MCQA) approach. Although this method is widely used in benchmark evaluations, it may not fully capture the complexity and variability of real-world usage scenarios. To enhance the robustness of our findings, we will incorporate various evaluation methods, thereby ensuring a more comprehensive analysis in the future. Our framework tSMC has demonstrated efficacy in evaluating the symmetric moral consistency of various models, highlighting their high ethical standards. Nonetheless, to further augment the comprehensiveness of our evaluations, future work will include testing with a more diverse ethical dataset. This expanded dataset will encompass a broader range of topics and professions by building on the strengths of our current methodology. We also notice the existence of additional biases in LLMs (e.g., length bias). In future work, we aim to extend our framework to address other biases.

Acknowledgments

This work was supported by Key Programs of Guangdong Province under Grant 2021QN02X166. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding parties.

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Appendix

A Example templates used for experiment

Figure 8: Input format templates for open-source LLMs when option IDs are AB . Few-shot examples are optional. Option IDs may also vary between C and D or other choices.

Figure 9: Input format templates for open-source LLMs for removing option IDs tasks. Few-shot examples are optional.

Figure 10: Input formats for GPT-3.5-turbo and Gemini-1.0-pro for removing option IDs tasks. Few-shot examples are optional.

B Model's results when option IDs are CD

Selection Bias

LOW-AMBIGUITY

HIGH-AMBIGUITY

CS

OS

FS

CS

OS

FS

Phi-2

86.6

86.9 (+0.30)

82.1 (-5.50)

33.1

40.7 (+7.60)

82.8 (+49.7)

Gemma-2b

61.7

99.7

(+38.0)

54.7 (-7.00)

37.2

88.1 (+50.9)

32.2 (-5.00)

Gemma-7b

84.6

28.1 (-56.5)

99.4

(+14.8)

53.7

25.9 (-27.8)

75.3 (+21.6)

Llama-2-7b

33.8

88.9 (+55.1)

89.8 (+56.0)

20.0

50.4 (+30.4)

60.1 (+40.1)

Llama-2-13b

97.4

98.0 (+0.60)

48.2 (-49.2)

65.7

46.2 (-19.5)

60.6 (-5.10)

Llama-2-70b

90.7

90.7 (-0.00)

43.2 (-47.5)

25.6

12.5 (-13.1)

89.7

(+64.1)

Llama-3-8b

52.4

80.2 (+27.8)

98.8 (+46.4)

12.9

19.9 (+7.00)

81.8 (+61.9)

Llama-3-70b

97.4

96.2 (-1.20)

98.3 (+0.90)

81.0

90.1 (+9.10)

76.5 (-4.50)

Mistral-7B

90.1

90.4 (+0.30)

91.1 (+1.00)

32.5

50.1 (+17.6)

83.2 (+50.7)

Mixtral-8x7B

100

99.7

(-0.30)

99.1 (-0.90)

85.6

91.9

(+6.30)

84.3 (-1.30)

phi-2

gemma-2b

gemma-7b

Llama-2-7b

Llama-2-13b

Llama-2-70b

Llama-3-8b

Llama-3-70b

Mistral-7B

Mixtral-8x7B

Gpt-3.5-turbo

Gemini-1.0-pro

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

Selection Bias

(a) Low-Ambiguity Moral Scenarios

(b) High-Ambiguity Moral Scenarios

0.6

0.8

1.0

0

200

400

600

800

Llama-2-13b-chat-hf

0.6

0.8

1.0

0

20

40

60

80

100

Llama-2-70b-chat-hf

0.6

0.8

1.0

0

100

200

300

Llama-2-7b-chat-hf

0.6

0.8

1.0

0

500

1000

1500

2000

2500

Llama-3-70b-Instruct

0.6

0.8

1.0

0

50

100

150

Llama-3-8b-Instruct

0.6

0.8

1.0

0

50

100

150

200

0.6

0.8

1.0

0

500

1000

1500

2000

2500

Mixtral-8x7B-Instruct-v0.1

0.6

0.8

1.0

0

500

1000

1500

gemma-2b-it

0.6

0.8

1.0

0

500

1000

1500

2000

gemma-7b-it

0.6

0.8

1.0

0

20

40

60

80

phi-2

0.6

0.8

1.0

0

50

100

150

Llama-2-13b-chat-hf

0.6

0.8

1.0

0

25

50

75

100

125

Llama-2-70b-chat-hf

0.6

0.8

1.0

0

20

40

60

80

100

Llama-2-7b-chat-hf

0.6

0.8

1.0

0

500

1000

1500

Llama-3-70b-Instruct

0.6

0.8

1.0

0

20

40

60

80

Llama-3-8b-Instruct

0.6

0.8

1.0

0

20

40

60

80

100

0.6

0.8

1.0

0

500

1000

1500

2000

Mixtral-8x7B-Instruct-v0.1

0.6

0.8

1.0

0

500

1000

1500

gemma-2b-it

0.6

0.8

1.0

0

500

1000

1500

gemma-7b-it

0.6

0.8

0

20

40

60

80

100

phi-2

C Bias Mitigation Effect of tSMC Framework on Low-ambiguity Moral Scenarios

phi-2

gemma-2b

gemma-7b

Llama-2-7b

Llama-2-13b

Llama-2-70b

Llama-3-8b

Llama-3-70b

Mistral-7B

Mixtral-8x7B

0.0

0.1

0.2

0.3

0.4

Absolute Difference between AB and CD

tSMC(

=0.005)

tSMC(

=0.1)

tSMC(

=0.2)

tSMC(

=0.3)

Context Swap

D Results on MMLU high_school_us_history subject

E Models' Links

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a framework called tSMC that uses KL divergence to measure the consistency of 12 LLMs on a moral question dataset. They find that the LLMs are more consistent on low-ambiguity questions, and less so on high-ambiguity questions. They also find that LLMs are susceptible to both position and selection bias, which their proposed measure corrects for.\n\nSTRENGTHS:\nI liked the investigation into confidence distributions instead of only prompt-based approaches. Bold sentences throughout helped to make clear the important takeaways.\n\nWEAKNESSES:\nThe central premise of this paper I find to be a bit confusing: why is moral consistency desired? Why is higher consistency framed as “better” (L185) and good throughout? From reading Figure 1, I’m not convinced any answer is right (e.g., what if the son is 30 years old and in a state where smoking weed is legal?). What if the answers are consistently more immoral, certainly the “right answer” is also relevant here rather than only consistency in isolation? Also, what about this framework is specific to moral questions, if any? Could it be generalized to consistency measures in general? All of these questions were on my mind as I read the paper.\n\nOther weaknesses:\n- L47-51: I would like a lot more explanation of this metric if it is being introduced here. What is the “standard method”? As well as some intuition to understand Table 1 which is presented here. If “Avg” is a measure of consistency, how should I be thinking about “Std,” which is sometimes also treated as a measure of consistency. Is it instead to be treated as some sort of confidence measure in the consistency?\n- Why is “A(B)” and “C(D)” used? Is this common? I feel like I normally would just say A vs B for a two-option multiple choice.\n- L84-85: “commonly encountered in practical applications” —> what is the evidence or citation for this?\n- L134: “context swap” and “full swap” have not been defined yet but are just used here\n- L156: I find the naming of this as “true moral consistency” to be a bit of an overclaim, if anything it is “adjusted moral consistency” since while it may account for position and selection bias, it doesn’t account for other points of bias, e.g., prompt word choice.\n- Table 2: if [9] is the main point of comparison, this should really be explained more as well as the details of what exactly is different about your method. Also, just because your measure is different from their method, how do we assess which is a “better” measure?\n- I would appreciate consistency (pun intended), in how you refer to the same phenomenon as either “inconsistency” or “bias” (e.g., L219-221)\n- L231-236: is this also true of Llama-2-7b? This feels like a potential overclaim\n- I loved the use of LLM confidence scores in 2.4, but I what I really wanted to see here was a direct comparison of the confidence scores to the consistency scores. Are they trying to measure the same thing? Do they exhibit the same thing?\nIn this particular paper, I think the related work might have made more sense earlier in the paper to set context, as well as explain more about [9].\n\nQUESTIONS:\nBeyond the questions I listed above in weaknesses (as they prevented me from fully buying into the paper), I am also wondering whether your method would scale to questions with more than two MC answer choices?\n\nLIMITATIONS:\nAddressed", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presented the impact of bias (position and selection biases) on the symmetric moral consistency of Large Language Models (LLMs) and revealed the inconsistent moral behavior in LLMs. This is significant as LLMs may not adhere to moral principles, which could harm the downstream tasks. A simple yet effective assessment framework tSMC was proposed to uncover\nthese biases and evaluate the true moral consistency of LLMs, highlighting the need to address these issues.\n\nSTRENGTHS:\n1. Critical insights into LLMs' inconsistent moral behavior that could raise ethical concerns across different decision-making tasks. Such behavior shows that the LLMs lack sufficient consistency in moral scenarios, which may hinder the progress of real-world deployment.\n\n2. A practical framework based on the Kullback–Leibler divergence that gauges the effects of these biases and effectively pinpoints LLMs’ true Symmetric Moral Consistency. \n\n3. Interesting findings are presented. For example, Such consistency is influenced by position bias and selection bias rather than LLMs' intrinsic abilities. This finding contrasts with prior research that shows LLMs prefer specific option IDs, which has important practical implications for guiding the LLMs' development. Furthermore, high-ambiguity and low-ambiguity scenarios bring varied consistency across different LLMs. LLMs tend to exhibit lower confidence in high-ambiguity scenarios, raising the attention to enhancing moral decisions' confidence level.\n\nWEAKNESSES:\n1. This framework is an assessment framework, but in line 3, the paper claims \"the framework mitigates .. biases\". Need more clarification to explain whether and how the framework mitigates these biases. \n\n2. Equation 3 seems valid, but more explanations and examples are needed.\n\nQUESTIONS:\nN/A\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-08"}

comments: SUMMARY:
The authors propose a “tSMC” framework to evaluate the moral consistency of LLMs. They apply this framework to 12 LLMs with a range of capabilities and find specific biases that significantly impact the consistency of certain models.

STRENGTHS:
- The authors find a large problem with moral consistency with LLMs which could lead to deployment of these models being problematic
- The presentation of the paper is clear and easy to follow
- tSMC could be generalized to evaluate consistency in other areas

WEAKNESSES:
No major weaknesses - the paper was a pleasure to read!

QUESTIONS:
- How did the authors select the parameter \alpha in equation 3?
- The authors claimed that the reason that “Llama-2-13b and Llama-2-70b exhibit both high position bias and selection bias 232 simultaneously” was due to having similar training data. Did the authors consider alternative hypotheses for this finding, for example both models having similar post-training methods?
- How do the authors believe these results (and further results using tSMC) will guide the development of future models?

LIMITATIONS:
The authors address the limitations.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-12

comments: SUMMARY:
This paper explores an important problem of symmetric moral consistency of Large Language Models and finds the inconsistency of moral decisions across varied scenarios, which brings the critical concern for the real-world deployment of LLMs. An assessment framework, tSMC, is proposed to address this issue by pinpointing moral consistency quantitatively. Extensive experiments on various models are conducted, which shows its effectiveness.

STRENGTHS:
1. This paper is well-structured and easy to follow. 
2. Adhering to moral principles and maintaining consistency becomes an urgent issue for the LLMs' development. This paper tackles this timely research problem from an interesting dimension, which offers valuable and deep insights.  
3. This paper reveals the subtle challenges of high-ambiguity scenarios to LLMs. The technique of leveraging Kullback–Leibler divergence effectively measures the effects of this complex problem. 
4. Extensive assessment is conducted to demonstrate the severity of LLMs' inconsistent moral behavior and the effectiveness of the proposed framework.

WEAKNESSES:
1. Clarification: (1) in lines 124-125, "the consistency performance of LLMs shows a low correlation with parameter size." can you explain it in more detail? ;  (2) Need to clarify the boundary between high-ambiguity and low-ambiguity scenarios. 

2. Generalization: This paper needs to discuss the generalization to more biases or the implications of extending the framework to more biases.

QUESTIONS:
see weakness

LIMITATIONS:
The authors have discussed their limitations in the Conclusion, and I also acknowledge their future plans.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-13

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper evaluates selection and position bias in large language models moral consistency.\n\nThe introduction, which expands over four pages, It is split between motivation case and the contributions. The introduction has one table, one prompt example, a diagram and two figures. It finalizes with an enumeration of the contributions.\n\nSection 2, presents the assessment framework along the choice of data set, models, evaluation and experiments\n\nSection 3, presents the related work\n\nSection 4, the conclusions\n\nSTRENGTHS:\nThe authors claim that the work is the first to investigate the issue of selection and positioned bias in large language models which appears to be true. \n\nTheyr work is based/inspired in a work found last year at NeurIPS. And their contribution seems sensible.\n\nWEAKNESSES:\nThe title doesn't provide much information about the paper. It seems a bit of a click bait.\n\nFrom the abstract, I don't know what is tSMC. The authors in generally use abbreviations without introducing them. This issue also holds for CS OS FS that first used and then introduced.\n\nThe following sentence doesn't show clear dicotomical difference. \n> Specifically, LLMs show more consistency in scenarios with clear moral answers compared to those where no choice is morally perfect\n\nCounterxample: An answer can be morally clear and still not morally perfect. I am unsure if perfect is the right wording here.\n\nIs this what [9] does? It's unclear here what is related work and what is the authors contribution?\n>Metric We adopt the standard method to investigate LLMs’ symmetric moral consistency [9]. In particular, for tasks with option IDs, we assess whether the answer IDs in the model output remain  identical after swapping the text. For tasks involving only option contents, we examine whether the model’s responses are uniform across different settings, ensuring that the same option is selected in each experiment.\n\n\nThis nomenclature of AC or BD it's unclear to me. What is this mention in the paper? I only see a AB CD. From Figure 1.\n> Your answer vocabulary is limited to A(C) or B(D).\n\nI found that the motivating case (figrue 1) particularly the moral dilemma, in my opinion doesnt present as a real moral dilemma as there as there is a lot of information missing. \n> You are driving home from work and see your son in a public park smoking weed with friends. Which of the following...\n\nIt could be be that your son is old enough or that weed is legal. \nEven if that its the situation, its unclear why it's a moral dilemma, or if its legal dilemma or a parentting one if any. It may not be a problem in some cultures.\n\nThe overall structure of the paper is not very clear.\n\nQUESTIONS:\nCould you clarify specifically what is the model dilemma in the motivating case example?\n\nWhy the \"true in \"true symmetric moral consistency \"and not just \"symmetric moral consistency\"? Its because the position bias and selection bias  are eliminated in the evaluation?\n\nL304. Why the transformer architecture? The methodoloy doesnt seem dependent on the architecture. \n> In this paper, bias refers to LLMs’ systematic error arising from the transformer architecture or other factors\n\nLIMITATIONS:\nThe authors don't state any limitations of their work explicitly.\n\nAs a reviewer, I am unfamiliar with the existing work on position and selection bias in LLMs.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-15"}