$\textit{Who Speaks Matters}$: Analysing the Influence of the Speaker's Ethnicity on Hate Classification (2410.20490v1)

Abstract: LLMs offer a lucrative promise for scalable content moderation, including hate speech detection. However, they are also known to be brittle and biased against marginalised communities and dialects. This requires their applications to high-stakes tasks like hate speech detection to be critically scrutinized. In this work, we investigate the robustness of hate speech classification using LLMs, particularly when explicit and implicit markers of the speaker's ethnicity are injected into the input. For the explicit markers, we inject a phrase that mentions the speaker's identity. For the implicit markers, we inject dialectal features. By analysing how frequently model outputs flip in the presence of these markers, we reveal varying degrees of brittleness across 4 popular LLMs and 5 ethnicities. We find that the presence of implicit dialect markers in inputs causes model outputs to flip more than the presence of explicit markers. Further, the percentage of flips varies across ethnicities. Finally, we find that larger models are more robust. Our findings indicate the need for exercising caution in deploying LLMs for high-stakes tasks like hate speech detection.

• The paper demonstrates that introducing implicit ethnic markers, such as dialectal features, significantly alters hate speech classification outcomes.
• The study employs experiments on four LLMs with a dataset from social media, revealing larger models are more robust than smaller ones.
• The findings underscore risks of disparate impacts on marginalized communities, urging the development of more inclusive training data.

Examination of LLM Robustness in Ethnically Diverse Hate Speech Detection

The paper "Who Speaks Matters: Analysing the Influence of the Speaker’s Ethnicity on Hate Classification" investigates how LLMs handle hate speech detection when implicitly and explicitly marked with various ethnic cues. The authors emphasize the importance of this issue given the well-documented biases against marginalized communities and dialects in artificial intelligence applications. This research is pivotal for ensuring fair and unbiased language technology deployment in content moderation.

Methodology Overview

The paper focuses on evaluating four popular LLMs: Llama-3-8B, Llama-3-70B, GPT-3.5-turbo, and GPT-4-turbo. A dataset composed of 3000 unique sentences from platforms such as Twitter, Reddit, and 4Chan is utilized. This dataset includes 600 labeled as hateful and 2400 as not hateful. To analyze model robustness, the authors introduce explicit and implicit ethnic markers into the text inputs. Explicit markers specify the speaker's ethnicity, while implicit markers involve injecting dialectal features.

Key Findings

1. Model Brittleness and Ethnic Markers: The paper reveals that models flip their hate speech classification outputs significantly more when implicit ethnic markers, i.e., dialectal features, are introduced compared to explicit markers. This brittleness highlights inherent biases within the pre-training corpora of these models, which often lack sufficient diversity in ethnic dialects.
2. Model Robustness and Size: Larger models, such as Llama-3-70B and GPT-4-turbo, display greater robustness, with fewer flips observed compared to smaller models like Llama-3-8B. This suggests that model capacity and diversity in training data may contribute to reducing biases.
3. Variation Across Ethnicities: The research notes variability in flip percentages across different ethnicities, with British, Indian, and Singaporean dialects showing higher flip rates. This variance underscores the need for careful consideration of ethnic diversity in LLMs.
4. Potential for Disparate Impact: Notably, model outputs for originally non-hateful inputs tend to flip to hateful more frequently for African-American and Jamaican dialects, signaling the risk of disproportionate representation and potential adverse impacts on specific communities.

Implications and Future Directions

The findings of this paper carry significant implications for the application of LLMs in high-stakes tasks. The brittleness and biases uncovered necessitate a cautious approach to deploying LLMs in content moderation and hate speech detection. Ensuring linguistic justice requires addressing the limitations in the representation of diverse dialects within training corpora and models. Future research could explore expanding multilingual datasets and developing more inclusive training paradigms to mitigate these biases.

Limitations and Research Opportunities

The paper identifies several limitations, such as reliance on limited dialect data and the number of models examined. Extending the analysis to other models and incorporating diverse datasets could offer a more comprehensive understanding of LLM behaviors in hate speech detection. Investigating automated methods for dialect translation and moderation tailored to cultural contexts could be a fruitful area for further exploration.

In conclusion, the paper effectively highlights critical areas where current LLMs fall short in handling ethnically diverse inputs in hate speech detection tasks. The insights provided offer a foundation for future work aimed at enhancing model robustness and fairness, ultimately contributing to the responsible deployment of AI in socially sensitive applications.