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Detecting Bias in Large Language Models: Fine-tuned KcBERT by Jun Koo Lee and Tai-Myoung Chung

Core Concepts
Societal bias exists in Korean language models due to language-dependent characteristics.
The rapid advancement of large language models (LLMs) has led to concerns about societal bias, especially in online offensive language. This paper investigates ethnic, gender, and racial biases in a model fine-tuned with Korean comments using Bidirectional Encoder Representations from Transformers (KcBERT) and KOLD data. The study quantitatively evaluates biases using LPBS and CBS metrics, showing a reduction in ethnic bias but significant changes in gender and racial biases. Two methods are proposed to mitigate societal bias: data balancing during pre-training and Debiasing Regularization during training. Experimental analysis highlights the need for preemptive measures in bias mitigation.
LPBS adopts a template-based approach similar to DisCo, calculating the bias degree by comparing the probabilities of predicting a specific attribute or target when the [MASK] token is predicted by LLMs. CBS generalizes metrics for multi-class targets and measures the variance of bias scores normalized by the logarithm of probabilities.
"We define such harm as societal bias and assess ethnic, gender, and racial biases in a model fine-tuned with Korean comments." "Our contribution lies in demonstrating that societal bias exists in Korean language models due to language-dependent characteristics." "Experimental analysis comparing the biases of the two models through Korean demonstrates the need for preemptive measures in bias mitigation."

Key Insights Distilled From

by J. K. Lee,T.... at 03-19-2024
Detecting Bias in Large Language Models

Deeper Inquiries

How can societal biases be effectively mitigated across different languages?

To effectively mitigate societal biases across different languages, it is essential to implement a combination of pre-processing, in-training, and post-processing mitigation methods tailored to the specific language and cultural context. Pre-processing: Data Balancing: Adjusting the distribution of data by aligning occurrences of specific words and transforming harmful words into non-harmful ones. This helps in creating a more balanced dataset that reduces bias during training. In-training: Debiasing Regularization: Applying techniques like dropout and regularization to prevent biased learning of the model during training. By adjusting these parameters, the model can learn to reduce associations between specific attributes that lead to bias. Post-processing: Token Adjustment: Modifying probability distributions during decoding phase to select tokens with lower bias levels. Attention Redistribution: Redistributing attention weights based on potential associations between encoded bias and token representations. By combining these approaches while considering language-specific nuances, such as word usage patterns or cultural references, it is possible to effectively mitigate societal biases in large language models across different languages.

What are the potential implications of overlooking societal biases in large language models?

Overlooking societal biases in large language models can have significant negative consequences: Reinforcement of Discrimination: Biased models perpetuate stereotypes and discriminatory practices by generating outputs that reflect existing social prejudices. Impact on Decision-making Systems: Biases in language models can influence automated decision-making systems leading to unfair outcomes for individuals from marginalized groups. Erosion of Trust: Users may lose trust in AI technologies if they perceive them as promoting biased or discriminatory content, impacting adoption rates and credibility. Addressing societal biases is crucial not only for ethical considerations but also for ensuring fair representation and equitable treatment within AI systems.

How can advancements in natural language processing technology contribute to reducing social discrimination?

Advancements in natural language processing (NLP) technology offer several avenues for reducing social discrimination: Bias Detection Tools: Develop tools that can identify and quantify biases present within text data or generated outputs from NLP models. Fairness Metrics: Implement fairness metrics that evaluate model performance across diverse demographic groups, helping developers understand where disparities exist. 3.Ethical Guidelines: Establish clear ethical guidelines for developing NLP systems that prioritize fairness, transparency, accountability,and inclusivity. By integrating these advancements into NLP research and development processes, it becomes possible to create more inclusive AI systems that actively work towards reducing social discrimination rather than perpetuating it through biased algorithms and outputs.