Covert Bias: The Severity of Social Views’ Unalignment in Language Models Towards Implicit and Explicit Opinion (2024)

Abstract: While various approaches have recently been studied for bias identification, little is known about how implicit language that does not explicitly convey a viewpoint affects bias amplification in large language models. To examine the severity of bias toward a view, we evaluated the performance of two downstream tasks where the implicit and explicit knowledge of social groups were used. First, we present a stress test evaluation by using a biased model in edge cases of excessive bias scenarios. Then, we evaluate how LLMs calibrate linguistically in response to both implicit and explicit opinions when they are aligned with conflicting viewpoints. Our findings reveal a discrepancy in LLM performance in identifying implicit and explicit opinions, with a general tendency of bias toward explicit opinions of opposing stances. Moreover, the bias-aligned models generate more cautious responses using uncertainty phrases compared to the unaligned (zero-shot) base models. The direct, incautious responses of the unaligned models suggest a need for further refinement of decisiveness by incorporating uncertainty markers to enhance their reliability, especially on socially nuanced topics with high subjectivity.

## introduction large language models perpetuate biases found in the training data, which constitute the pretraining models' main building blocks @xcite . recent studies looked into the severity of bias in the models @xcite . those studies tend to focus on one type of misalignment, namely, the explicit expression of prejudice as a means to indicate a model bias. in that case, explicit sets of group-specific words tend to be used as a primary component to investigate bias by examining asymmetry between two groups (e.g., women and men) and their association with a set of attributes (e.g., home and work). this kind of spurious correlation generally appears in naturalistic data collected for training the models @xcite . thus, some work has been made to understand the bias caused by these spurious correlations, such as studying the concept level of data to understand biases @xcite . however, these concept-based framework data may be subject to hidden biases, particularly with regard to ambiguous or arguable labeling judgments and especially in the case of subjective opinions about a concept @xcite . therefore, we conducted a focused examination of the impact of a viewpoint-based task to determine the extent of bias severity within implicit and explicit opinions regarding social prejudice issues. specifically, we sought to answer the following questions: (q 1 ) does the discrepancy between implicit and explicit opinion affect the model behavior toward a specific social group? (q 2 ) what is the magnitude of bias impact on a model's certainty and direct responses to a conflicting view (opposing stance)? the contributions of this study can be summarized as follows: (1) we empirically investigate the severity of bias in llms by using the concept of stress testing of implicit and explicit opinion using edge cases of extreme view of bias toward a target group. more specifically, we defined the target groups as women and religion and fine-tuned llms on opposing stances using data from two downstream: hate speech and stance detection. (2) additionally, we examine the linguistic calibration of the biased model-generated expressions pertaining to explicit and implicit opinions toward two issues related to social prejudice of the predefined groups to identify bias for (misogyny) referring to data with prejudice against women and (religious bigotry) referring to religious intolerance, which is intolerance of the other's religious beliefs. ## related work bias amplification is a well-known phenomenon in which a model aggravates the stereotypes presented in its training data @xcite . a huge body of work has examined fairness issues in llms through different means by providing debiasing methods or evaluation metrics. for instance, work by @xcite introduced bias mitigation methods by fine-tuning pre-trained bert models on text authored by demographic groups and used the sentence encoder association test to measure gender and racial bias by measuring the association sets of target concepts and attributes. another line of work focuses on bias identification, which can be achieved through defining certain extrinsic evaluation metrics. some recent work has investigated implicit bias @xcite )by assigning a persona to "user" instructions to provide information about the social group target as an identity assignment. further work by @xcite proposed a measure of implicit bias in llms as a prompt-based method called the implicit association test. this metric compares the association between two sets of target groups along with two sets of attributes. stress testing has been employed in various evaluation scenarios, such as in natural language inference @xcite , to push models beyond their normal functioning limits and identify weaknesses. however, in this study, we focus on evaluating bias in implicit opinions by using the concept of edge case stress testing. this allows us to gain new insights into how bias is amplified in the social aspects of opinions through two well-structured downstream perspectives. ## experimental setup the focus of this study is on language indicative of viewpoints to examine how bias toward a target is also aligned in the models through implicit expressions. by "target," we refer to a social group or aspect of opinion formulation toward a topic. in our case, this refers to opinions toward "women" in misogyny topics and "religion" in religious bigotry topics. we conducted experiments on hate speech and stance detection tasks, which provided a well-formulated setting based on the view toward a specific target or topic in either implicit or explicit expressions. for stance detection, the task was primarily formulated as stance(text, target) = {favor, against, none}. similarly, in hate speech detection, the task was formulated to identify opinionated hate speech toward a target as hate(text, target) = {hateful, neutral}. ## discussion in this work, we revisit bias in opinion-based tasks, focusing on the implicit type of these expressions by using the concept of edge cases to evaluate llms. first, we investigated how the edge case of a biased model trained on conflict views performs in two downstream tasks, stance and hate speech detection (q 1 ). we found that the amount of performance degradation can vary by task; in some cases, the degradation was severe, especially in the stance detection task. we then studied how the biased model affected certainty as a linguistic calibration of llms in generating responses to stance and hateful comments with (q 2 ). overall, the biased fine-tuned models tend to use more uncertainty phrases than unaligned zero-shot llms. most of the recent work on confidence and uncertainty commonly focuses on the correctness of a response to factual questions as a core component to evaluate uncertainty @xcite . our findings reinforce the need to enhance the opinion-based responses of llms, especially for implicit language. ## conclusion and future work this work emphasizes the importance of evaluating implicitly expressed opinions to distinguish bias amplification in llms, especially regarding social issues. the incautious approach seen in direct responses suggests a need for further refinement to enhance models' decisiveness without compromising accuracy and reliability. we hope the finding of this study paves the way for a further evaluation of the opinion type of the direct responses (infavor or against), and the certainty level of these responses will provide a deeper understanding of llms' behavior in responding to social base topics with different levels of subjectivity and variations.