Evaluating the Fairness of Large Language Models as Rankers: An Empirical Study

Основные понятия

Large Language Models (LLMs) exhibit biases in their ranking outcomes, underrepresenting historically marginalized groups. This study empirically evaluates the fairness of popular LLMs, including GPT-3.5, GPT-4, Llama2-13b, and Mistral-7b, as text rankers using listwise and pairwise evaluation methods.

Аннотация

This study presents an empirical evaluation of the fairness of Large Language Models (LLMs) when used as text rankers. The authors focus on assessing the representation of binary protected attributes, such as gender and geographic location, in the ranking outcomes of these models.

The study utilizes the TREC Fair Ranking dataset, which contains queries and associated documents annotated with protected attributes. The authors conduct both listwise and pairwise evaluations to assess fairness from different perspectives:

Listwise Evaluation:

• Measures the exposure of protected and non-protected groups in the ranking results, using group fairness metrics.
• Analyzes the performance of LLMs and neural ranking models (MonoT5 and MonoBERT) on precision and fairness.
• Finds that while neural rankers exhibit higher precision, LLMs show more balanced treatment of protected and non-protected groups in listwise rankings.
• Observes query-side biases, where LLMs and neural rankers tend to favor female and European queries over male and non-European ones.

Pairwise Evaluation:

• Examines the ranking preferences of LLMs when presented with pairs of relevant or irrelevant items from protected and non-protected groups.
• Reveals that LLMs, such as GPT-3.5, GPT-4, Mistral-7b, and Llama2-13b, exhibit biases towards certain groups, particularly in the ranking of irrelevant items.

To address the fairness issues observed in the pairwise evaluation, the authors fine-tune the Mistral-7b model using the Low-Rank Adaptation (LoRA) technique. The results show that the LoRA-adjusted model achieves fairness ratios closer to the ideal benchmark of 1.0, indicating more equitable treatment of protected and non-protected groups.

The study highlights the importance of considering fairness alongside effectiveness when evaluating LLMs as rankers, and provides a comprehensive benchmark for assessing the fairness of these models.

Статистика

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Цитаты

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Ключевые выводы из

Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers

by Yuan Wang,Xu... в arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03192.pdf

Дополнительные вопросы

How can the fairness evaluation framework proposed in this study be extended to other types of protected attributes beyond gender and geography, such as race, age, or socioeconomic status?

In extending the fairness evaluation framework to other protected attributes, such as race, age, or socioeconomic status, the key lies in adapting the existing methodologies to accommodate the unique characteristics and challenges associated with each attribute. Here are some ways to extend the framework:

Data Representation: Modify the dataset to include annotations for the additional protected attributes. Ensure that the dataset is balanced in terms of representation across all attributes to avoid biases in the evaluation process.

Metrics Development: Develop specific fairness metrics tailored to each protected attribute. For example, when evaluating race, metrics could focus on the exposure of different racial groups in the ranking outcomes. Similarly, for age, metrics could assess the treatment of different age groups in the rankings.

Prompt Design: Adjust the prompt templates to include references to the new protected attributes. This will enable the evaluation of how LLMs handle queries related to race, age, or socioeconomic status and rank documents associated with these attributes.

Fine-Tuning Strategies: Implement fine-tuning strategies that target the biases related to the specific protected attributes being evaluated. For instance, fine-tuning the models with datasets that emphasize fair treatment of different racial groups can help mitigate biases in ranking outcomes related to race.

Pairwise Evaluation: Extend the pairwise evaluation method to compare items from different racial groups, age groups, or socioeconomic statuses. This will provide insights into how LLMs rank items from diverse backgrounds relative to each other.

By customizing the evaluation framework to address a broader range of protected attributes, researchers can gain a more comprehensive understanding of the fairness of LLMs across various demographic dimensions.

How might the potential limitations of the TREC Fair Ranking dataset used in this study impact the generalizability of the findings?

The TREC Fair Ranking dataset, while valuable for evaluating fairness in LLM-based ranking systems, has certain limitations that could impact the generalizability of the findings:

Limited Attribute Coverage: The dataset may not encompass a wide range of protected attributes beyond gender and geography. This limitation could restrict the applicability of the findings to other demographic dimensions like race, age, or socioeconomic status.

Dataset Bias: The dataset may contain inherent biases or imbalances that could influence the evaluation results. Biases in the dataset could lead to skewed conclusions about the fairness of LLMs, affecting the generalizability of the findings to real-world scenarios.

Sample Size: The size of the dataset may not be large enough to capture the full diversity of queries and documents encountered in practical applications. A small sample size could limit the robustness and generalizability of the study findings.

Contextual Constraints: The specific context of the TREC Fair Ranking dataset, focused on WikiProject coordinators and Wikipedia articles, may not fully represent the complexities of ranking tasks in other domains or applications. This context-specific nature could restrict the generalizability of the findings.

Temporal Relevance: The dataset's temporal relevance may be a factor, as fairness considerations and biases in LLMs can evolve over time. Findings based on an outdated dataset may not accurately reflect the current landscape of LLM-based ranking systems.

Considering these limitations, researchers should interpret the findings from the TREC Fair Ranking dataset with caution and seek to validate them across diverse datasets and scenarios to enhance the generalizability of the study results.

Given the observed biases in LLM-based ranking systems, what alternative approaches or architectures could be explored to develop more equitable and inclusive ranking models?

To address the biases observed in LLM-based ranking systems and promote fairness and inclusivity, several alternative approaches and architectures can be explored:

Fairness Constraints: Integrate fairness constraints into the training process of LLMs to ensure equitable treatment of different demographic groups. By penalizing biased predictions during training, models can learn to make fairer ranking decisions.

Diverse Training Data: Enhance the diversity of training data to include a wide range of perspectives and representations from various demographic groups. A more diverse training dataset can help mitigate biases and promote inclusivity in ranking outcomes.

Adversarial Training: Implement adversarial training techniques to identify and mitigate biases in LLMs. Adversarial training involves training a model to generate counterexamples that expose and correct biases in the ranking decisions of the primary model.

Interpretable Models: Explore interpretable models that provide insights into the decision-making process of LLMs. By understanding how models arrive at their rankings, researchers can identify and address biases more effectively.

Ensemble Approaches: Combine multiple LLMs or ranking models to create an ensemble that leverages diverse perspectives and mitigates individual biases. Ensemble methods can enhance the robustness and fairness of ranking systems.

Human-in-the-Loop Systems: Incorporate human oversight and feedback loops in ranking systems to detect and correct biases. Human-in-the-loop approaches can provide a checks-and-balances mechanism to ensure fair and inclusive rankings.

Regularization Techniques: Apply regularization techniques that encourage models to learn more balanced representations of different demographic groups. Regularization can help prevent models from overfitting to biased patterns in the data.

By exploring these alternative approaches and architectures, researchers can work towards developing more equitable and inclusive LLM-based ranking models that prioritize fairness and diversity in their decision-making processes.