Attention Speaks Volumes: Localizing and Mitigating Bias in Language Models (2410.22517v1)

Abstract: We explore the internal mechanisms of how bias emerges in LLMs when provided with ambiguous comparative prompts: inputs that compare or enforce choosing between two or more entities without providing clear context for preference. Most approaches for bias mitigation focus on either post-hoc analysis or data augmentation. However, these are transient solutions, without addressing the root cause: the model itself. Numerous prior works show the influence of the attention module towards steering generations. We believe that analyzing attention is also crucial for understanding bias, as it provides insight into how the LLM distributes its focus across different entities and how this contributes to biased decisions. To this end, we first introduce a metric to quantify the LLM's preference for one entity over another. We then propose $\texttt{ATLAS}$ (Attention-based Targeted Layer Analysis and Scaling), a technique to localize bias to specific layers of the LLM by analyzing attention scores and then reduce bias by scaling attention in these biased layers. To evaluate our method, we conduct experiments across 3 datasets (BBQ, Crows-Pairs, and WinoGender) using $\texttt{GPT-2 XL}$ (1.5B), $\texttt{GPT-J}$ (6B), $\texttt{LLaMA-2}$ (7B) and $\texttt{LLaMA-3}$ (8B). Our experiments demonstrate that bias is concentrated in the later layers, typically around the last third. We also show how $\texttt{ATLAS}$ effectively mitigates bias through targeted interventions without compromising downstream performance and an average increase of only 0.82% in perplexity when the intervention is applied. We see an average improvement of 0.28 points in the bias score across all the datasets.

• The paper presents Atlas, a novel attention-based method that localizes bias in LLMs by analyzing attention distributions.
• It employs targeted scaling of later-layer attention scores to reduce preferential bias while maintaining overall performance.
• Experimental results demonstrate consistent bias reduction across datasets (BBQ, CrowS-Pairs, WinoGender) with minimal perplexity impact.

Analyzing and Mitigating Bias in LLMs with Atlas

The paper "Attention Speaks Volumes: Localizing and Mitigating Bias in LLMs" presents a meticulous approach to identifying and reducing bias in LLMs. The authors scrutinize how biases emerge within LLMs, particularly when models are confronted with ambiguous comparative prompts. These prompts require a choice between entities without offering explicit context for preference. The research addresses bias at the root level—the model itself—by leveraging the attention mechanism. Unlike traditional approaches focused mainly on post-hoc analysis or data augmentation, this paper provides insights into the intrinsic processes of LLMs and proposes targeted interventions reflecting a deeper understanding of the model's behavior.

Methodology

This paper introduces a novel method called Atlas (Attention-based Targeted Layer Analysis and Scaling), aimed at localizing and mitigating biases in LLMs. Atlas uses the notion that biases, reflected in the model's outputs, are influenced by the distribution of attention scores, particularly towards the end layers of the model. The methodology consists of a two-step process:

1. Localization of Bias: Atlas introduces a metric to quantify the model's preference for one entity over another. It analyzes attention scores, particularly at the last tokens, to disentangle how the model's focus might introduce bias in decision-making. The technique involves identifying which layers harbor biased attention distributions. The investigation reveals that biases are predominantly located in the latter layers, especially the last third of layers in LLMs.
2. Mitigation of Bias: Once localized, the paper employs a mechanism to scale attention scores within these biased layers, thereby reducing preferential treatment aligned with biased outputs. The attention scaling is precise and selective, fostering mitigation without jeopardizing the overall performance of the LLM.

Experimental Results

The authors validate their approach using three datasets: BBQ, CrowS-Pairs, and WinoGender. They employ several LLMs, namely GPT-2 XL, GPT-J, LLaMA-2, and LLaMA-3. The results demonstrate that Atlas consistently reduces bias across various models and datasets. Notably, there is an average improvement of 0.28 points in the bias score, with minimal impact on perplexity (average increase of only 0.82\%), suggesting that the method efficiently balances mitigation and performance preservation.

Implications and Future Directions

The implications of this research are manifold. As LLMs become increasingly integrated into applications influencing sensitive domains such as hiring, law, and healthcare, understanding and mitigating biases becomes crucial. The proposed Atlas framework not only contributes towards more fair and ethical AI deployments but also provides a basis for further explorations into the inner workings of LLMs to improve other areas of model interpretability and ethical AI practices.

By demonstrating a method where bias can be addressed without fundamentally altering downstream performance, Atlas paves the way for scalable and effective bias mitigation strategies. Looking forward, this approach can be expanded and adapted to other forms of bias beyond those explored in this paper. Future work might also explore the method's extension in dynamic learning environments and assess its impact across more diverse and complex datasets. The methodology offers a foundation upon which more nuanced and comprehensive models can be developed, incorporating fairness as a built-in feature rather than an afterthought.

The paper crucially advances our understanding of LLM bias, laying the groundwork for both theoretical advancements and practical implementations in responsible AI design.