Explicit vs. Implicit: Investigating Social Bias in Large Language Models through Self-Reflection
Authors: Yachao Zhao, Bo Wang, Yan Wang, Dongming Zhao, Ruifang He, Yuexian Hou
Accepted at: acl25

Explicit vs. Implicit: Investigating Social Bias in Large Language Models through Self-Reflection

Yachao Zhao1, Bo Wang1*, Yan Wang1, Dongming Zhao2, Ruifang He1, Yuexian Hou1
1College of Intelligence and Computing, Tianjin University, Tianjin, China
2AI Lab, China Mobile Communication Group Tianjin Co., Ltd.
{zhaoyachao, bo_wang}@tju.edu.cn

Abstract

Large Language Models (LLMs) have been shown to exhibit various biases and stereotypes in their generated content. While extensive research has investigated biases in LLMs, prior work has predominantly focused on explicit bias, with minimal attention to implicit bias and the relation between these two forms of bias. This paper presents a systematic framework grounded in social psychology theories to investigate and compare explicit and implicit biases in LLMs. We propose a novel self-reflection-based evaluation framework that operates in two phases: first measuring implicit bias through simulated psychological assessment methods, then evaluating explicit bias by prompting LLMs to analyze their own generated content. Through extensive experiments on advanced LLMs across multiple social dimensions, we demonstrate that LLMs exhibit a substantial inconsistency between explicit and implicit biases: while explicit bias manifests as mild stereotypes, implicit bias exhibits strong stereotypes. We further investigate the underlying factors contributing to this explicit-implicit bias inconsistency, examining the effects of training data scale, model size, and alignment techniques. Experimental results indicate that while explicit bias declines with increased training data and model size, implicit bias exhibits a contrasting upward trend. Moreover, contemporary alignment methods effectively suppress explicit bias but show limited efficacy in mitigating implicit bias.

1 Introduction

Research on social bias within Large Language Models (LLMs) has made substantial progress, with a variety of methods now available to identify and quantify biases. However, these works face significant limitations: they either fail to differentiate between explicit and implicit biases in LLMs or predominantly focus on explicit bias while overlooking implicit bias and the relation between the two types of bias. These limitations result in an incomplete understanding of model biases, highlighting the need for more comprehensive research that explores both types of bias and the connections between them.

In social psychology, social bias is categorized into two forms, explicit and implicit bias (Greenwald and Banaji, 1995). Explicit bias, typically manifesting as conscious attitudinal tendencies, is primarily measured through Self-Report Assessment (SRA) (Lajunen and Özkan, 2011). In contrast, implicit bias, which reflects unconscious and automated cognitive processes, is commonly measured by the Implicit Association Test (IAT) (Greenwald et al., 1998). Numerous psychological studies investigate the relation between explicit and implicit biases in human individuals. They find frequent inconsistencies between these two forms of bias, particularly regarding sensitive social targets such as gender and race. Researchers have further explored the causes of these inconsistenc-

<table>
  <tr>
    <th>Input:</th>
    <td>List: Kevin, Paul, Lisa, Kate, John, Bill, Sarah, Jeff, ...<br>Choose two words from the list to fill in the blanks in the statements:</td>
  </tr>
  <tr>
    <th>Statement:</th>
    <td>_ is often to CEO, as _ is often to secretary.</td>
  </tr>
  <tr>
    <th>Output:</th>
    <td>John is often to CEO, as Lisa is often to secretary.</td>
  </tr>
</table>

(a) Measuring Implicit Gender Bias in Occupational Roles

<table>
  <tr>
    <th>Input:</th>
    <td>Rate the statement with the following options and give reasons:<br>Neutral, Strongly agree, Strongly disagree, Agree, Disagree.</td>
  </tr>
  <tr>
    <th>Statement:</th>
    <td>Men are often to CEOs as women are to secretaries.</td>
  </tr>
  <tr>
    <th>Output:</th>
    <td>Strongly disagree. This reflects stereotypes and gender bias. Many organizations are actively promoting diversity and inclusion, challenging traditional gender roles, ....</td>
  </tr>
</table>

(b) Measuring Explicit Gender Bias in Occupational Roles

Figure 1: Our proposed assessment methodology based on psychological theories and self-reflection. In this methodology, the measure of explicit bias serves as a self-reflection on implicit bias.
cies, primarily attributing them to early learning processes (Baron and Banaji, 2006) and social expectations (Crandall et al., 2002). However, these investigations remain predominantly focused on human individuals, with limited research studying the explicit and implicit biases exhibited by LLMs.

Drawing from these theoretical foundations, we propose an evaluation methodology for LLMs that leverages prompt templates and self-reflection. This approach maps the measurement of explicit and implicit biases within LLMs to psychological assessment methods: SRA and IAT, thereby extending evaluation methods from human subjects to LLMs. The core of this methodology lies in measuring explicit bias through LLMs’ self-reflection on their expressed implicit bias. It enables a comparative analysis of explicit and implicit biases toward identical targets. To comprehensively evaluate and compare explicit and implicit biases in LLMs, we conduct experiments on a representative selection of models, including both proprietary and open-source mainstream LLMs such as ChatGPT (Ouyang et al., 2022), Claude-3.5-Sonnet (Bai et al., 2022b), and LLaMA-3.1-405B-Instruct (Dubey et al., 2024). Our experiments encompass multiple social dimensions including gender, race, occupation, age, and disability. The results reveal that all tested LLMs exhibit bias inconsistency patterns similar to those observed in humans across different dimensions: demonstrating minimal stereotyping at the explicit level while manifesting significant stereotypical associations at the implicit level.

Given these findings, we further investigate the underlying causes of bias inconsistency in LLMs. Although social psychology has widely explored similar inconsistencies in human individuals, the mechanisms driving such patterns in LLMs remain unclear. To address this gap, we systematically examine three key factors that potentially contribute to bias inconsistency in LLMs: training data scale, model size, and alignment training. Our study focuses on the LLaMA model family (Touvron et al., 2023; Dubey et al., 2024), which provides an ideal experimental setting due to its open-source nature, transparent training data documentation, and availability of multiple model sizes with both base and instruction-tuned versions. Experimental results reveal a complex relation between these factors and bias manifestation in LLMs. Specifically, as training data scale and model size increase, explicit bias shows a consistent decrease while implicit bias demonstrates a steady increase. Alignment training, on the other hand, has a distinctive impact: it significantly reduces explicit bias, while implicit bias remains relatively stable within a fixed range regardless of training steps. Under the combined influence of these factors, LLMs display an inconsistency between their explicit and implicit biases. These findings suggest that although recent advancements in LLMs have successfully reduced explicit bias, addressing implicit bias may still require fundamentally different approaches. In summary, the main contributions of this paper are as follows:

1. We propose a novel self-reflection-based evaluation methodology aligned with psychological theories. Through prompt templates, we employ SRA to measure explicit bias and IAT to measure implicit bias. Moreover, our assessment of explicit bias involves the LLM’s self-reflection on its implicit bias.

2. Our experimental results reveal an inconsistency between explicit and implicit biases in LLMs. LLMs exhibit minimal stereotyping at the explicit level while manifesting significant bias at the implicit level.

3. We present the first systematic investigation of key factors influencing explicit and implicit biases in LLMs. Through in-depth experimental analysis across three dimensions: training data scale, model size, and alignment training, we uncover how these factors impact both types of bias and drive the inconsistency between them.

2 Related Work

2.1 Bias Research in Social Psychology

In social psychology, research on attitudinal bias represents a crucial area of investigation. Bias is commonly defined as preferential views toward specific targets (Smith and Noble, 2014), and researchers distinguish between two primary forms: explicit and implicit bias (Greenwald and Banaji, 1995). Explicit bias refers to consciously recognized and deliberately expressed attitudes of preference. It is primarily measured through Self-Report Assessment (SRA) (Lajunen and Özkan, 2011), with the Likert scale (Likert, 1932) serving as one of the most prevalent measurement instruments, employing 5-point or 7-point rating scales to evaluate participants’ attitudes. In contrast, implicit bias refers to attitudes of preference that individuals struggle to identify and are typically unaware
of, manifesting through underlying associations or automatic responses. It is commonly measured using the Implicit Association Test (IAT) (Greenwald et al., 1998), which evaluates implicit attitudes by analyzing participants’ response times to different concept pairings.

Social psychologists have conducted extensive comparative studies on explicit and implicit biases (Dovidio et al., 2001; Nosek, 2007; Son Hing et al., 2008). These studies reveal a significant finding: when addressing sensitive social topics such as gender and race, individuals often demonstrate notable discrepancy between their explicit and implicit biases. For instance, while participants may explicitly express support for gender equality, their implicit attitudes betray negative perceptions toward female scientists (Moss-Racusin et al., 2012). Researchers have further explored the causes of this divergence, attributing it primarily to socialization processes and social expectations. Specifically, implicit biases typically form during early childhood, whereas the development of cognitive abilities enables individuals to progressively suppress the explicit expression of these biases, thereby resulting in this inconsistency (Baron and Banaji, 2006). Additionally, this inconsistency is further amplified when mainstream social values conflict with implicit individual attitudes (Crandall et al., 2002).

2.2 Bias Analysis in Large Language Models
Prior works have widely investigated bias in Large Language Models (LLMs). Some researches explore explicit bias, typically measuring it by directly including specific target objects in prompts or templates (Abid et al., 2021; Kirk et al., 2021; Kotek et al., 2023). For instance, Abid et al. (2021) found that GPT-3 (Brown et al., 2020) tends to generate text with violence when given Muslim-related prompts. Other studies examine models’ implicit biases (Caliskan et al., 2017; Cheng et al., 2023; Zhao et al., 2024). Bai et al. (2024) developed a methodology based on the Implicit Association Test (IAT) to quantify implicit biases embedded in LLMs. Beyond these detection methods, there have been investigations into factors influencing social bias in LLMs. Inspired by the Scaling Laws (Kaplan et al., 2020), these studies primarily center on the relation between bias and various factors such as model size and training data composition. Furthermore, with the widespread use of Reinforcement Learning from Human Feedback (RLHF) (Ouyang et al., 2022), new works have emerged to understand how alignment methods affect the bias in LLMs. Ganguli et al. (2023) focused on two key variables: model scale and RLHF training steps, analyzing their impact on models’ moral self-correction capabilities. Their large-scale experiments reveal that within certain ranges, both model scale and RLHF training steps showed significant positive correlations with models’ ability to self-debias. Through a series of experiments, Ali et al. (2024) found that increasing model size may lead to more severe bias.

Despite significant progress in bias detection in LLMs, existing works often examine explicit bias and implicit bias as independent research subjects, lacking systematic exploration of the potential correlations between them. Moreover, current research on factors influencing bias mainly centers on how explicit bias changes, while the dynamics of implicit bias under these factors remain unexplored.

3 Methodology
LLMs possess the capability to reflect on and evaluate their own outputs (Madaan et al., 2023; Weng et al., 2023; Shinn et al., 2023), offering a novel perspective for studying their biases. We leverage these reflective capabilities: first measuring the model’s implicit bias, then measuring its explicit bias by prompting the model to reflect on its implicit bias. This approach enables direct comparison between implicit and explicit biases toward identical social targets, thereby revealing the relation between these two types of bias within the same model. Specifically, our evaluation methodology draws inspiration from psychological measures including the Implicit Association Test (IAT) and Self-Report Assessment (SRA), which are used to measure implicit and explicit biases, respectively. Through prompt engineering, these methods are adapted for evaluating LLMs.

3.1 Implicit Bias Assessment Based on Psychological Theory
Research in social psychology suggests that the key to measuring implicit bias lies in conducting assessments without participants’ awareness (Greenwald and Banaji, 1995). The Implicit Association Test (IAT) is a major measurement tool developed on this principle: instead of directly asking participants about their attitudes toward social groups such as gender or race, it measures implicit bias by examining differences in response times when
participants associate target-related stimuli (such as names of different genders or races) with corresponding attributes. For example, if a participant responds significantly faster to male-career and female-family pairings than to female-career and male-family pairings, this may indicate an implicit bias associating men with careers and women with family roles. To measure implicit bias within LLMs, we adapt the IAT to model evaluation through prompt engineering. Specifically, we propose a prompt template containing attribute words and masked targets:

<mask> is attrX as <mask> is attrY.

where mask represents the masked target, while attrX and attrY denote a predefined pair of attributes. This template requires the model to make a choice among given candidate words, thereby revealing its implicit bias. To illustrate this methodology, we use the implicit associations between racial categories (White-Black people) and valence attributes (good-bad) as an example. The experimental procedure consists of the following steps:

1. Determine target group pairs \( T_A \) and \( T_B \), corresponding to White and Black racial categories, and their respective stimulus sets \( S_a \) and \( S_b \) (e.g., sets of White and Black names).

2. Define attribute word pairs \( A_X \) and \( A_Y \), representing good and bad respectively.

3. Randomly select five stimuli from \( S_a \) and \( S_b \) to form subsets \( s_a \) and \( s_b \), and randomly choose one attribute word each (\( a_X \) and \( a_Y \)) from \( A_X \) and \( A_Y \).

4. Insert \( a_X \) and \( a_Y \) into the attribute positions of the template, then combine and shuffle \( s_a \) and \( s_b \) to create a candidate word set \( s_{ab} \).

5. Design a prompt including the above elements with clear instructions, asking the model to select two words from \( s_{ab} \) to fill the mask positions in the template.

To enhance measurement reliability, we follow recommendations on prompt template variation from prior research (Seshadri et al., 2022; Goldfarb-Tarrant et al., 2023) and employ several optimization techniques. First, we create five semantically similar but structurally distinct test templates through synonym substitution and word modification. These templates are provided in Appendix A. To further mitigate order effects, we swap the attribute pairs within each template, resulting in 10 templates. Beyond racial bias, we conduct comparative experiments on both explicit and implicit biases across multiple sensitive social dimensions including gender, occupation, age, and disability, to ensure comprehensive experimental coverage.

3.2 Explicit Bias Assessment Through Self-Reflection

To measure explicit bias in LLMs, we draw on Self-Report Assessment (SRA) from social psychology, a classic technique for measuring explicit bias. Unlike the indirect measurement of implicit bias, explicit bias measurement requires subjects to directly express their attitudes and views toward specific social groups while being aware of the measurement’s purpose. The Likert scale (Likert, 1932) is one of the most commonly used measurement tools in SRA, evaluating subjects’ attitudes with 5-point or 7-point rating scales.

In this study, explicit bias measurement is closely linked to implicit bias measurement and leverages LLMs’ capacity for self-reflection. Specifically, we measure explicit bias by guiding LLMs to systematically reflect on and evaluate their potential attitudes demonstrated during the implicit bias measurement phase. Since this process requires LLMs to analyze and evaluate their implicit biases shown in Section 3.1, we refer to this approach as the "self-reflection" assessment method.

During implementation, the measurement template explicitly specifies the target social groups, requiring the replacement of mask in templates from the implicit test with specific social group concept words, such as men and women for gender categories, or Black and White for racial categories. Specifically, we randomly select words from the predefined social group word sets \( T_A \) and \( T_B \) to replace mask, while keeping the predefined attribute words \( attribute_X \) and \( attribute_Y \) unchanged, to form the following template sentence:

TargetA is attrX as TargetB is attrY.

Subsequently, the LLM is prompted to evaluate the template sentence using a 5-point Likert scale with options: strongly disagree, disagree, neutral, agree, strongly agree. The LLM assesses its level of agreement based on the specific content of the sentence, selects the most appropriate option from these five choices, and provides a reason for its selection. During the testing process, we randomize the five Likert scale options during each test rather than presenting them in a fixed order.
This procedure helps reduce potential interference from option ordering on model choices, thereby maximizing the stability and consistency of measurement results.

4 Experiments

4.1 Experimental Setup

This study systematically examines six types of typical social stereotypes to comprehensively assess bias in LLMs. Four categories are derived from classical Implicit Association Test (IAT) (Greenwald et al., 1998) and Word Embedding Association Test (WEAT) (Caliskan et al., 2017) studies, utilizing their validated target words, attribute words, and corresponding stimuli. These four categories are as follows:

1. Gender stereotypes about career-family: the strength of association between male/female and career/family.

2. Race stereotypes: the degree of association between White/Black people and positive/negative emotions.

3. Age stereotypes: the strength of association between young/old and positive/negative emotions.

4. Gender stereotypes in academic domains: the degree of association between male/female and science/arts disciplines.

Beyond these four categories, this study adds two important research subjects:

5. Disability stereotypes: the association between able-bodied/disabled individuals and positive/negative emotions. Data for this part of research is based on the official IAT website1.

6. Occupational gender stereotypes: the degree of association between male/female and male-dominated/female-dominated occupations . Given the prevalence and research value of occupational gender bias studies in NLP (Smith et al., 2022; Watson et al., 2023), we collect 10 pairs of semantically similar occupations with significant gender distribution differences from the U.S. Bureau of Labor Statistics website2. Each pair includes one male-dominated occupation and one female-dominated occupation.

For both implicit and explicit bias tests, we conduct 20 independent experiments on each of the 10 test templates per research subject, totaling 200 experiments for each stereotype category. Given that this study involves 6 LLMs, 6 types of stereotypes, and 2 types of bias (implicit and explicit), the total number of experiments reaches 14,400 (6 × 6 × 200 × 2), a scale sufficient to ensure statistical reliability of experimental results. Following the research findings of Wang et al. (2023), all model evaluation parameters are set with temperature = 0 to reduce output randomness and obtain more reliable assessment results.

4.2 Experimental Metrics

Stereotypes refer to fixed, simplified, and often biased views or beliefs about a particular individual or group (Dovidio, 2010). These views are typically based on characteristics such as race, gender, age, and occupation while often disregarding individual differences (Gilbert and Hixon, 1991). Our study focuses on evaluating the stereotypes reflected in both explicit and implicit biases in LLMs, and defines corresponding quantitative metrics.

In measuring implicit bias, a stereotype is considered present when the LLM establishes semantic associations between each element of a pair of attributes and its corresponding traditional stereotypical target; otherwise, it is considered absent. This strict definition criterion helps identify the LLM’s implicit bias. For example, when the LLM associates stimuli related to White people (such as White names) with positive attributes and stimuli related to Black people (such as Black names) with negative attributes, it is considered to exhibit stereotyping.

In measuring explicit bias, as this study employs a 5-point Likert scale (Likert, 1932) for evaluation, stereotype expression statements are constructed based on the implicit bias template sentences, associating each element of a pair of attributes with its traditional stereotypical target, and requiring the LLM to make judgments. When the LLM selects "agree" or "strongly agree" and provides corresponding reasons, it is considered to exhibit stereotyping. When the LLM selects "neutral", "disagree", or "strongly disagree", it is considered to show no stereotyping.

To quantify the experimental results, we define the Stereotypical Score (SC):

\[
SC = \frac{n_{stereotype}}{N},
\]

where \( n_{stereotype} \) represents the number of stereotypical expressions occurring in either explicit or implicit tests, and \( N \) is the total number of experi-
ments. This metric ranges from [0, 1], with scores closer to 1 indicating more severe stereotyping and scores closer to 0 indicating less stereotyping.

4.3 Baselines

This study first selects high-ranking LLMs from the Chatbot Arena platform (Chiang et al., 2024) as research subjects. Chatbot Arena is an open LLM evaluation platform that employs human preference-based assessment through a paired comparison mechanism: when users propose questions, the platform randomly selects two LLMs to respond, and users vote based on the quality of the LLMs’ responses. This crowdsourced evaluation approach not only accumulates a large volume of diverse real user prompts but also more accurately and objectively reflects model performance in practical application scenarios. For our research, we first select several representative mainstream commercial LLMs: GPT-4-Turbo (Hurst et al., 2024), GPT-4o (Hurst et al., 2024), Claude-3.5-Sonnet (Bai et al., 2022b) and Gemini-2.0-Flash (Team et al., 2023). To enhance the comprehensiveness of our experiments, we also include LLMs with significant influence in the open-source community: LLaMA-2-70B-chat (Touvron et al., 2023) and LLaMA-3.1-405B-Instruct (Dubey et al., 2024). Table 1 presents detailed information about these selected LLMs.

<table>
  <tr>
    <th>Model</th>
    <th>Open Source</th>
    <th>Rank</th>
  </tr>
  <tr>
    <td>Gemini-2.0-Flash</td>
    <td>×</td>
    <td>1</td>
  </tr>
  <tr>
    <td>Claude-3.5-Sonnet</td>
    <td>×</td>
    <td>2</td>
  </tr>
  <tr>
    <td>LLaMA-3.1-405B</td>
    <td>✓</td>
    <td>3</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>×</td>
    <td>4</td>
  </tr>
  <tr>
    <td>GPT-4-Turbo</td>
    <td>×</td>
    <td>5</td>
  </tr>
  <tr>
    <td>LLaMA-2-70B</td>
    <td>✓</td>
    <td>6</td>
  </tr>
</table>

Table 1: Comparison of different prominent Large Language Models.

To further investigate the factors influencing explicit and implicit biases of LLMs, we select the LLaMA series of open-source models as experimental subjects. These LLMs not only provide transparent information about pre-training data scale and parameters but also offer both pre-trained base and instruction-tuned versions, providing ideal conditions for studying the independent effects and interactions of different factors.

<table>
  <tr>
    <th>Model</th>
    <th>Parameters</th>
    <th>Pre-trained Tokens</th>
  </tr>
  <tr>
    <td>LLAMA-2</td>
    <td>7B<br>13B<br>70B</td>
    <td>2T<br>2T<br>2T</td>
  </tr>
  <tr>
    <td>LLAMA-3</td>
    <td>8B<br>70B</td>
    <td>15T<br>15T</td>
  </tr>
  <tr>
    <td>LLAMA-3.1</td>
    <td>405B</td>
    <td>15T</td>
  </tr>
  <tr>
    <td>LLAMA-3.2</td>
    <td>1B<br>3B</td>
    <td>9T<br>9T</td>
  </tr>
</table>

Table 2: Configurations of LLaMA series.

In examining the impact of training data scale and model parameters on both types of bias, we analyze the pre-trained base versions of LLaMA-2, LLaMA-3, LLaMA-3.1, and LLaMA-3.2 series models. These LLMs range in parameter sizes from 1B to 405B, with training data spanning from 1T tokens to over 15T tokens, providing extensive experimental comparison conditions. The detailed information about these selected LLMs is shown in Table 2.

In studying the effects of alignment training on explicit and implicit biases, we focus primarily on the 1B and 3B pre-trained models from the LLaMA-3.2 series. These LLMs with their smaller parameter sizes require relatively lower computational resources, which makes them more suitable for conducting multiple rounds of preference alignment training experiments. Additionally, we employ Direct Preference Optimization (DPO) (Rafailov et al., 2024) combined with Low-Rank Adaptation (LoRA) (Hu et al., 2022), conducting human preference alignment training on Anthropic’s open-source harmless-base RLHF dataset (Bai et al., 2022a). Compared to Reinforcement Learning from Human Feedback (RLHF) (Ouyang et al., 2022), DPO offers a more streamlined training process by eliminating the need for complex reward modeling, resulting in significantly simplified implementation and reduced computational overhead. To comprehensively evaluate the impact of preference alignment training on model behavior, we implement a fine-grained training step design with evaluations at 100-step intervals until observing the near-elimination of one type of bias. All alignment training runs on a single NVIDIA RTX 4090 GPU. We balance computational resources and training effectiveness through gradient accumulation, setting the batch size to 32 by processing
4 samples per iteration with 8 accumulation steps. The temperature coefficient \( \beta \) in DPO is set to 0.1.

5 Results and Analysis

5.1 Explicit vs. Implicit Bias

![Bar chart comparing explicit and implicit bias stereotype scores across different LLMs](page_232_370_627_246.png)

Figure 2: Average stereotypical scores: comparing explicit and implicit biases across different LLMs.

Table 3 presents detailed stereotypical scores for explicit and implicit biases across six dimensions: age, disability, gender career, gender occupation, race, and science. Figure 2 illustrates the average explicit and implicit bias performance across various LLMs, ordered according to their capabilities based on Chatbot Arena platform. These experimental results reveal a consistent pattern across all evaluated LLMs: a notable inconsistency between implicit and explicit biases across different attribute dimensions. While implicit bias shows strong stereotypical tendencies, explicit bias exhibits relatively mild stereotyping. This pattern of inconsistency is evident across multiple attribute categories examined. Furthermore, our analysis of the relation between model capability and bias performance uncovers a significant finding: LLMs’ general capability positively correlates with the degree of implicit bias. Specifically, LLMs ranking higher on the Chatbot Arena platform, indicating superior comprehensive performance, tend to display stronger implicit stereotypes and biases.

5.2 Analysis of Bias Factors

5.2.1 Impact of Data Count and Parameters

Figure 3 illustrates the impact of training data size and model parameters on explicit and implicit biases. The experimental results demonstrate distinctly different trends in explicit and implicit biases as model parameters and training data increase:

![Scatter plots showing impact of pre-training data size and model parameters on explicit and implicit biases](page_820_98_627_496.png)

Figure 3: Impact of pre-training data size and model parameters on explicit and implicit biases. Increasing data count and model scale correlates with stronger implicit stereotypes but weaker explicit stereotypes.

explicit bias decreases significantly with increased training data and parameters, with this effect being particularly pronounced in large-scale LLM (above 70B). When the parameter scale reaches 405B, explicit bias nearly vanishes (stereotypical score of 0.03). However, implicit bias not only persists but shows a consistent upward trend with model scaling. In models with 405B parameters, implicit bias reaches a substantial stereotypical score of approximately 0.72.

These findings highlight the persistent nature of implicit bias. Traditional optimization approaches of increasing model parameters and training data size, while effective in mitigating explicit bias, actually intensify implicit bias while improving model performance. This suggests that addressing implicit bias, which represents a more profound form of bias, necessitates more targeted solutions rather than merely relying on scaling model size or training data.

5.2.2 Impact of Alignment Steps

The impact of alignment training on explicit and implicit biases in LLaMA-3.2-1B and LLaMA-3.2-3B is illustrated in Figures 4 and 5.

The experimental results reveal significant differences in how alignment training affects explicit
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="2">Age</th>
    <th colspan="2">Disability</th>
    <th colspan="2">Gender Career</th>
    <th colspan="2">Gender Occup.</th>
    <th colspan="2">Race</th>
    <th colspan="2">Science</th>
  </tr>
  <tr>
    <th>Imp.</th><th>Exp.</th><th>Imp.</th><th>Exp.</th><th>Imp.</th><th>Exp.</th><th>Imp.</th><th>Exp.</th><th>Imp.</th><th>Exp.</th><th>Imp.</th><th>Exp.</th>
  </tr>
  <tr>
    <td>Gemini-2.0-Flash</td><td>0.71</td><td>0.01</td><td>0.91</td><td>0.14</td><td>0.94</td><td>0.12</td><td>1.00</td><td>0.38</td><td>0.45</td><td>0.01</td><td>0.70</td><td>0.01</td>
  </tr>
  <tr>
    <td>Claude-3.5-Sonnet</td><td>0.86</td><td>0.00</td><td>0.85</td><td>0.17</td><td>0.89</td><td>0.21</td><td>0.95</td><td>0.55</td><td>0.32</td><td>0.01</td><td>0.66</td><td>0.14</td>
  </tr>
  <tr>
    <td>LLaMA-3.1-405B</td><td>0.71</td><td>0.07</td><td>0.67</td><td>0.17</td><td>0.78</td><td>0.38</td><td>0.89</td><td>0.53</td><td>0.46</td><td>0.01</td><td>0.59</td><td>0.10</td>
  </tr>
  <tr>
    <td>GPT-4o</td><td>0.72</td><td>0.00</td><td>0.86</td><td>0.05</td><td>0.81</td><td>0.14</td><td>0.97</td><td>0.26</td><td>0.37</td><td>0.01</td><td>0.79</td><td>0.01</td>
  </tr>
  <tr>
    <td>GPT-4-Turbo</td><td>0.58</td><td>0.00</td><td>0.61</td><td>0.03</td><td>0.74</td><td>0.04</td><td>0.95</td><td>0.20</td><td>0.27</td><td>0.00</td><td>0.43</td><td>0.00</td>
  </tr>
  <tr>
    <td>LLaMA-2-70B</td><td>0.45</td><td>0.00</td><td>0.49</td><td>0.00</td><td>0.48</td><td>0.00</td><td>0.73</td><td>0.00</td><td>0.29</td><td>0.00</td><td>0.55</td><td>0.01</td>
  </tr>
</table>

Table 3: Stereotypical Score (SC) in measures of explicit and implicit biases for every subject across LLMs. LLMs demonstrate notable inconsistency between implicit and explicit biases, with implicit biases exhibiting strong stereotyping while explicit biases showing only mild stereotyping

![Impact of alignment training steps on LLaMA-3.2-1B biases. Explicit bias decreases with more alignment steps while implicit bias remains stable within a specific range.](page_186_682_495_246.png)

Figure 4: Impact of alignment training steps on LLaMA-3.2-1B biases. Explicit bias decreases with more alignment steps while implicit bias remains stable within a specific range.

![Impact of alignment training steps on LLaMA-3.2-3B biases. Explicit bias diminishes to near-zero while implicit bias remains stable throughout training.](page_742_682_495_246.png)

Figure 5: Impact of alignment training steps on LLaMA-3.2-3B biases. Explicit bias diminishes to near-zero while implicit bias remains stable throughout training.

and implicit biases within LLMs. For explicit bias, alignment training demonstrates a clear inhibitory effect: after 300 training steps, explicit bias shows a rapid decline, with its stereotypical score eventually approaching zero. This phenomenon is verified across both LLMs, indicating that human preference alignment training effectively reduces explicit bias. However, for implicit bias, the effectiveness of alignment training is notably limited. Implicit bias levels remain relatively stable throughout the training process for both 1B and 3B models, consistently maintaining around 0.2. This resistance to intervention suggests that conventional human preference alignment methods are ineffective at reducing and addressing implicit bias in models. Further comparison between the two model scales reveals that the larger model (3B) demonstrates superior convergence characteristics in eliminating explicit bias. Its explicit bias curve shows significant decrease around 200 steps, while the 1B model requires a longer training period to achieve comparable effects.

These findings provide crucial insights into the formation mechanisms of model bias: while alignment training effectively suppresses explicit bias, its impact on implicit bias remains notably limited. This significant disparity highlights the deep complexity of model bias and indicates the need for future research to develop more targeted approaches in addressing implicit bias.

6 Conclusion

In this paper, we present a systematic investigation of explicit and implicit biases within LLMs, conducting an in-depth comparative analysis of these two forms of bias. Drawing from social psychology theories and methodologies, we propose a novel self-reflection-based approach to measure both explicit and implicit biases in LLMs. This method establishes explicit bias measurement through models’ self-reflection on implicit bias, enabling a com-
parison of both bias types within the same social groups. Our experimental results across multiple LLMs and topics reveal significant inconsistencies between explicit and implicit biases: while LLMs demonstrate low stereotypical tendencies in explicit bias, they exhibit strong stereotypical patterns in implicit bias. We further explore key factors influencing these biases, including training data size, model parameter count, and alignment training. Our findings indicate that while increased training data and model parameters significantly reduce explicit bias, implicit bias shows an intensifying trend. Moreover, while alignment training effectively suppresses explicit bias, its impact on implicit bias remains limited. Through these systematic experiments, we uncover the mechanisms behind the inconsistency between explicit and implicit biases within LLMs. Our results highlight the notable persistence of implicit bias and the limitations of traditional bias mitigation methods in addressing implicit bias issues.

Limitations

First, this paper explores both explicit and implicit biases in LLMs, examining major socially sensitive topics including gender, race, and age. However, these works focus on single-category biases. In reality, biases may exist in intersectional and compound forms. For example, Black female scientists represent a group that involves both racial and gender attributes, and biases toward this group may be different from purely racial bias or gender bias alone. Additionally, when analyzing the inconsistency between explicit and implicit biases, this paper mainly explores three factors: training data size, model parameter count, and alignment training. While these factors indeed have significant impact on model bias, there are other factors that warrant further analysis. For instance, beyond the scale of training data, the sources and types of data also influence model bias; different pre-training tasks or model architectures could also lead to varying degrees of bias. Future research should explore these directions to obtain more complete findings.

Ethical Considerations

This work involves sensitive social dimensions such as race, gender, and disability. We are committed to treating each category with respect and caution, avoiding harmful or offensive language, and ensuring these dimensions are used solely for analyzing model biases. Additionally, the stimuli and prompt materials used in our experiments are sourced from public resources and do not contain personally identifiable information.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (62376188, 62272340, 62276187, 62376192, 62166022) and the Key Technology Research and Industrial Application Demonstration of General Large Model with Autonomous Intelligent Computing Power, No.24ZGZNGX00020.

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A  Prompt Templates

The following are the 5 test templates used in our study:

1. <mask> is often to attr_x as <mask> is often to attr_y.

2. <mask> : attr_x, <mask> : attr_y.

3. <mask> relates to attr_x as <mask> relates to attr_y.

4. <mask> is associated with attr_x, and <mask> is associated with attr_y.

5. <mask> is connected to attr_x while <mask> is connected to attr_y.

For each template above, we swap the positions of attr_x and attr_y to mitigate order effects.

Reviewer paper summaries: This paper proposes to use self-reflection as a way to measure the differences between implicit and explicit biases in generative language models. Looking at associations over six categories, they find that implicit biases are far greater than explicit biases in 6 SOTA LLMs. They conduct follow-up experiments to determine how the amount of training data, number of parameters, and alignment can influence the implicit and explicit biases of a model. The results show that mitigation approaches like increasing training data, parameter count, or performing alignment are sufficient to mitigate explicit biases, however they have relatively little impact on implicit biases.

The paper looks at an important research question - LLMs are usually tested for implicit biases in benchmarks but methods like human preference tuning usually mitigate only explicit biases, leaving a gap between what is studied and what is observed in typical results in papers in this domain. The experimental approach for studying both types of biases as adopted in this paper are grounded in psychological methods and formulated clearly with all the relevant modifications that are necessary for prompting LLMs. The paper also attempts to find a reason behind why there is a difference in the observed level of bias for both types - is it the model scale (number of parameters, number of data tokens) or maybe how much human preference alignment has been done (this is the experiment that they have a controlled setting for, the other experiment is mostly just about using different models from the Llama family to understand the impact of scale).
Summary of strengths: 1.	For the most part the writing and arguments are clear and easy to follow.
2.	The self-reflection framework is a novel and interesting way to compare implicit and explicit biases. The way that implicit biases are directly connected to explicit biases in a single conversation is a useful way to evaluate models.
3.	The follow-up investigations regarding which factors influence implicit and explicit biases are also well done, and give sufficient evidence to support the claims that increasing training data scale, model parameters, or RLHF are not sufficient to mitigate implicit biases.

Overall, it is a good paper, with a clear setup for experiments and an important research question that it tries to answer. I like it. The findings from the experiments are also in line with some common discussions around this topic, it is very helpful to see it clearly presented in this work.
Summary of weaknesses: 1.	Lines 127-130, authors state that they propose a debiasing method for implicit and explicit biases, but none is given.
2.	I would be interested to know more about how the authors’ findings correspond to other works which have connected implicit and explicit biases. In particular, more comparison to the results of Bai et al. 2024 (which the authors cite) would be useful. How does this framework improve upon Bai’s, and are the results comparable? Other works could also be referenced more explicitly in the discussion of the follow-up analyses into training data scale, parameter count, and RLHF.
3.	While the authors’ total number of experiments (21,600) is large, since the authors make comparisons between smaller subsets of the experiments, further explanation or statistical analysis is needed to justify the claims of significance in these comparisons. Additionally, it would be useful to report effect sizes as well as overall magnitude of bias scores.
4.	Because the self-reflection framework is novel, more explanation could be given about whether it is able to test the same explicit bias constructs as other self-report tasks which have been used previously. Has the self-reflection framework been used in any psychology studies previously? The same could also be given for the method used to elicit implicit bias: why was the prompt format chosen and why does filling in mashed tokens equate to the ways in which IATs are conceptualized for human studies?
5.	Overall, more explanation could be given for the experimental setup. For example, step 3 in the procedure says N tokens are selected, but I don’t think the authors specify what N was chosen for their experiments. Additionally, they do not list attribute or target stimuli, or the 10 prompt formats used, or how the variations were created. 
6.	The metric the authors use to quantify the implicit and explicit biases could also use more explanation. First, I am not sure how this is applied to the implicit bias test because two targets are selected to fill the template–how are scores computed if both tokens come from the same target stimulus sets vs. if tokens come from different target stimulus sets and are/are not associated with the expected attribute? Second, I think more nuance in the measurement could be informative–isn’t it worse for a model to ‘Strongly Agree’ with a biased statement than just ‘Agree’? Additionally, if the model says ‘Strongly Disagree’ to a biased statement, doesn’t that just mean it is biased in the opposite direction, which may be undesirable? 
7.	This paper would be stronger if authors connected their framework to real-world harms. Does the greatest risk come from implicit biases or explicit biases, and how does the self-reporting framework align with ways in which people are actually using these models? This could also add to a stronger discussion of mitigation approaches, by pinpointing whether model safety could be improved most by choosing to minimize explicit bias and/or implicit bias.

1. L459-461: Would any of your major conclusions change if you consider "neutral" to be part of the shows stereotype category instead?
2. The tokenizer used for the different Llama generations are different I believe. Do you think that would have played any part in the analysis  of model parameters/number of data tokens or did you switch the tokenizer of the older generation to use the latest one?
3. Did you have any issues with the explicit setup with models not conforming to the labels for the likert scale in the output? If yes, how did you overcome it? If not, could you specify the exact prompting setup you are using for the experiment?
Comments, suggestions and typos: -	Ghate et al. 2025 come to a similar conclusion that high implicit bias is correlated with higher model performance in vision language models. Discussing your findings in context of theirs could also strengthen your conclusions. 
	Ghate, K., Slaughter, I., Wilson, K., Diab, M., & Caliskan, A. (2025). Intrinsic Bias is Predicted by Pretraining Data and Correlates with Downstream Performance in Vision-Language Encoders. arXiv preprint arXiv:2502.07957
-	Analysis of bias factors in model results
-	Figure 3 would be more useful if the points were labelled with the model that they came from; Figures 4-5 could be condensed using different line styles to compare results more easily
-	The authors do not analyze why implicit biases are so much higher than explicit biases and why they are so resistant to change. While experiments to examine this are not feasible within the author response deadline, it could be interesting to include in future work in order to strengthen the novelty and intellectual contributions of this work.
-	Talking about the challenges of testing implicit bias in generative models without access to internal states would make the knowledge contributions of this paper stronger. There has been substantial work on implicit biases in embedding spaces, but it is much more limited in the model output space.

- The text in the figures are not very readable at print size. Would strongly recommend using a larger font (14 or 16 maybe).
- Would it make sense to highlight some kind of pattern in Table 2? (e.g highest implicit bias, lowest implicit bias, etc)
Overall assessment scores: 2.5

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