Investigating Potential Biases in Language Models: Unexpected Findings on Ethnicity-Based Sentiment Analysis

Core Concepts

Language models may exhibit unexpected biases against the Caucasian/White ethnic group when using template-based datasets to measure sentiment analysis performance disparities, likely due to a mismatch between the pre-training data and the structure of the bias probes.

Abstract

The paper investigates potential biases in large language models (LLMs) by examining their performance on sentiment analysis tasks across different ethnic groups. The authors use several template-based datasets, including the Amazon, NS-Prompts, and Regard datasets, to measure the false positive rate (FPR) gaps for negative and positive sentiment predictions across different ethnicities.
The key findings are:

For most models, the Negative-Sentiment FPR gap for text associated with the Caucasian/White ethnicity is significantly above zero, indicating that the models more often misclassify positive or neutral sentiment examples for this group as negative compared to other groups.

The Positive-Sentiment FPR gap for the Caucasian/White group is statistically significantly negative, combined with the elevated Negative-Sentiment FPR gap. This suggests that the models tend to erroneously view examples from the Caucasian/White ethnicity as negative more often than other groups.

Similar patterns are observed for the African American and Asian groups, but to a smaller extent in the larger variants of the OPT and Llama2 models.

The authors hypothesize that these unexpected findings are not reflective of true biases in the LLMs, but rather an artifact of a mismatch between the structure of the template-based bias datasets, which explicitly mention ethnicity, and the underlying pre-training data of the LLMs, which often do not explicitly state the race/ethnicity due to reporting bias. This discrepancy may lead to the models treating the explicitly mentioned "White" or "Caucasian" text as out-of-domain or eccentric, resulting in the observed performance disparities.
The paper highlights the need to carefully consider the impact of reporting bias in pre-training data when using template-based datasets to measure bias in LLMs, as this can lead to misleading conclusions. The authors suggest exploring alternative approaches, such as using datasets that establish group membership through metadata or classification techniques rather than explicit mention, or incorporating multimodal models that may be less affected by reporting bias.

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Key Insights Distilled From

Reevaluating Bias Detection in Language Models

by Farnaz Kohan... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03471.pdf

Deeper Inquiries

How can we design bias measurement approaches that better account for reporting bias in pre-training data?

To design bias measurement approaches that better account for reporting bias in pre-training data, we need to consider the inherent mismatch between the templates used for bias quantification and the underlying pre-training data of the Language Models (LMs). One approach could be to move away from template-based bias probes that explicitly mention sensitive attributes like ethnicity to establish group membership. Instead, we can explore methods that infer group membership through metadata, self-identification, or classification techniques without explicitly mentioning the attribute in the text. By incorporating datasets explicitly correcting for reporting bias in the pre-training of LMs, we can align the bias measurement process more closely with the distribution of the training data. Additionally, using more sophisticated bias probes that do not rely on explicit mention of sensitive attributes can help mitigate the impact of reporting bias on bias measurement in LMs.

What are the potential limitations of using template-based datasets for bias quantification, and how can we address them?

Using template-based datasets for bias quantification can have limitations, especially when it comes to measuring biases related to sensitive attributes like ethnicity. One major limitation is the reliance on explicit mention of group membership in the templates, which may not align with the reporting bias present in the pre-training data of LMs. This can lead to misleading measurements and inaccurate assessments of bias. To address these limitations, we can explore alternative approaches that do not explicitly mention sensitive attributes in the templates. For example, we can use metadata, self-identification, or classification techniques to infer group membership without explicitly stating it in the text. Additionally, incorporating datasets that explicitly correct for reporting bias in the pre-training of LMs can help improve the alignment between bias measurement approaches and the underlying data distribution.

How might the integration of visual information into language models impact their susceptibility to reporting bias and improve their ability to accurately assess social biases?

The integration of visual information into language models can have a significant impact on their susceptibility to reporting bias and their ability to accurately assess social biases. By combining textual and visual data, multimodal models can provide a more comprehensive understanding of societal biases and their manifestation across different training data sources. This integration can potentially make the models more robust to the misalignment found in reporting bias present in pre-training data. Visual information can help provide context and additional cues that may not be present in text alone, allowing the models to make more informed and accurate assessments of social biases. Multimodal models may be less affected by reporting bias due to their ability to leverage knowledge extracted from distinct modalities, leading to more reliable and fair assessments of biases in language models.