Co$^2$PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning (2310.12490v1)

Abstract: Pre-trained LLMs are widely used in many important real-world applications. However, recent studies show that these models can encode social biases from large pre-training corpora and even amplify biases in downstream applications. To address this challenge, we propose Co$^2$PT, an efficient and effective debias-while-prompt tuning method for mitigating biases via counterfactual contrastive prompt tuning on downstream tasks. Our experiments conducted on three extrinsic bias benchmarks demonstrate the effectiveness of Co$^2$PT on bias mitigation during the prompt tuning process and its adaptability to existing upstream debiased LLMs. These findings indicate the strength of Co$^2$PT and provide promising avenues for further enhancement in bias mitigation on downstream tasks.

The paper presented, titled "Co$^2$PT: Mitigating Bias in Pre-trained LLMs through Counterfactual Contrastive Prompt Tuning," introduces a novel methodology aimed at addressing and reducing biases in pre-trained LLMs (PLMs). These models, which are extensively utilized in various real-world applications, have been shown to encapsulate and sometimes exacerbate social biases such as those related to gender, race, or religion from the large datasets they are trained on.

Proposed Technique: Co$^2$PT

Co$^2$PT stands for Counterfactual Contrastive Prompt Tuning and is designed as an efficient and effective method to mitigate biases by leveraging training data through contrastive learning techniques without requiring extensive re-training of the PLM itself.

Key Components of Co$^2$PT:

1. Counterfactual Data Augmentation: The technique creates counterfactual pairs from the training data by altering demographic identifiers in sentences to represent different groups. This aims to ensure that the model provides consistent outcomes irrespective of the demographic terminologies used.
2. Contrastive Learning Framework: Co$^2$PT integrates a contrastive objective that optimizes task-specific representations while keeping PLM parameters frozen. The framework aligns semantically similar sentences that differ only in demographic terms, ensuring fairer output.
3. Prompt Tuning: Continuous prompts are added to each layer of the PLM. Instead of modifying the PLM's architecture (which could lead to forgetting valuable pre-trained knowledge), Co$^2$PT focuses on learning debiased prompt representations that guide the PLM towards unbiased predictions.

Experimental Evaluation

The paper evaluates Co$^2$PT on various benchmarks specifically designed to measure extrinsic biases in downstream applications:

• Bias-STS-B: Modified to scrutinize gender biases by comparing semantic similarity scores in sentence-pairs featuring gendered terms.
• Bias-NLI: Examines gender-occupation biases in natural language inference tasks, assessing models on their deviation from neutrality in prediction probabilities.
• Bias-in-Bios: Investigates gender bias in profession classification by measuring disparities in true positive rates between different genders across occupations.

Results and Findings

Co$^2$PT demonstrates significant prowess in mitigating bias across these benchmarks while maintaining or even improving model performance on downstream tasks. Compared to existing debiasing methods, Co$^2$PT supports stronger reductions in bias scores, highlighting its effectiveness and flexibility. Noteworthy observations include:

• Co$^2$PT’s considerable improvement in bias metrics over traditional fine-tuning and prompt-tuning models, with lower bias observed in predictions across modified datasets.
• When integrated with existing upstream debiased models, Co$^2$PT further reduces biases encountered during downstream fine-tuning.
• The introduction of counterfactual data and contrastive objectives significantly contributes to the reduction of encoding bias, making Co$^2$PT an adaptable solution across diverse bias dimensions beyond binary gender.

Future Directions

The methodology invites further research into its applicability to non-gender and intersectional biases and the exploration of languages other than English. Awareness of these limitations suggests a pathway to enhancing Co$^2$PT’s universal application across varied NLP tasks and languages. By addressing these biases more holistically, Co$^2$PT contributes to the overarching goal of fairness and equity in AI-driven technologies.