insight - Summarization - # Fair Abstractive Summarization

Fair Abstractive Summarization: Measuring and Improving Representation of Diverse Perspectives

Q: How can we extend the fairness evaluation framework to other types of bias beyond the social attributes considered in this paper?

To extend the fairness evaluation framework to other types of bias beyond social attributes, we can consider incorporating additional dimensions of bias such as cultural, linguistic, or ideological biases. This can be achieved by identifying relevant attributes that are prone to bias in the context of the dataset or task at hand. For example, in a dataset related to healthcare, factors like socioeconomic status, geographical location, or access to healthcare services could be potential attributes contributing to bias. Furthermore, we can explore the intersectionality of multiple attributes to understand how biases may compound or interact with each other. By analyzing how different attributes intersect and influence the fairness of the generated summaries, we can develop a more comprehensive framework for evaluating bias in natural language generation models.

Q: How can we mitigate the potential biases in the training data of the LLMs that contribute to the unfairness in the generated summaries?

Mitigating biases in the training data of Language Models (LLMs) requires a multi-faceted approach: Data Preprocessing: Conduct thorough data preprocessing to identify and address biases in the training data. This may involve data augmentation, balancing datasets, or removing biased samples. Diverse Training Data: Ensure that the training data is diverse and representative of the population to minimize biases. Incorporate data from various sources and perspectives to reduce the impact of skewed data. Bias Detection Algorithms: Implement bias detection algorithms to identify and quantify biases in the training data. This can help in understanding the root causes of bias and taking corrective actions. De-biasing Techniques: Utilize de-biasing techniques such as adversarial training, counterfactual data augmentation, or fairness constraints during model training to reduce biases in the LLMs. Regular Auditing: Continuously audit the training data and model outputs for biases. Implement feedback loops to iteratively improve the fairness of the models.

Q: How can we design summarization models that are inherently fair, rather than relying on post-processing techniques to improve fairness?

Designing summarization models that are inherently fair involves integrating fairness considerations into every stage of the model development process: Fairness-Aware Training: Incorporate fairness constraints and objectives into the model training process. This can involve optimizing the model to generate summaries that maintain a balanced representation of different perspectives and attributes. Fairness by Design: Develop models that are inherently fair by design, considering fairness as a core principle during architecture design and hyperparameter tuning. This may involve incorporating fairness metrics into the loss function or model architecture. Diverse Training Data: Train the model on diverse and representative datasets to ensure that it learns to generate fair summaries across different attributes and perspectives. Interpretable Models: Design models that provide transparency and interpretability in their decision-making process. This can help in identifying and addressing biases during model development. Ethical Guidelines: Establish ethical guidelines and frameworks for developing fair summarization models, ensuring that ethical considerations are prioritized throughout the model development lifecycle.

Core Concepts

Generating abstractive summaries that comprehensively cover diverse perspectives without underrepresenting certain groups.

Abstract

This paper investigates the fairness of abstractive summarization by large language models (LLMs). The authors first formally define fairness in abstractive summarization as not underrepresenting perspectives of any groups of people. They then propose four reference-free automatic metrics to measure fairness by comparing the distribution of target and source perspectives.

The authors evaluate nine LLMs, including GPT, LLaMA, PaLM 2, and Claude, on six datasets covering diverse domains such as social media, online reviews, and recorded transcripts. The results show that both human-written reference summaries and LLM-generated summaries often suffer from low fairness.

The authors conduct a comprehensive analysis to identify common factors influencing fairness, such as decoding temperature and summary length. They also propose three simple but effective methods to improve fairness, including changing decoding temperature, adjusting summary length, and appending the definition of fairness to the instruction prompt.

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Stats

The paper uses six datasets covering diverse domains and attributes:

Claritin: Tweets about the drug Claritin, with gender as the attribute.
US Election: Tweets during the 2016 US presidential election, with political party as the attribute.
Yelp: Business reviews, with sentiment as the attribute.
Amazon: Product reviews, with rating as the attribute.
Supreme Court: Transcripts of Supreme Court oral arguments, with speaker as the attribute.
Intelligence Squared (IQ2): Recorded public debates, with speaker as the attribute.

Quotes

"A fair summary should provide a comprehensive coverage of diverse perspectives without underrepresenting certain groups."
"Results of our metrics and human evaluation show that neither humans nor LLMs could maintain the fairness of summaries."
"Prompt engineering and careful choosing of the temperature of LLMs can significantly improve the performance of fairness."

Key Insights Distilled From

Fair Abstractive Summarization of Diverse Perspectives

by Yusen Zhang,... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2311.07884.pdf

Fair Abstractive Summarization of Diverse Perspectives

Deeper Inquiries

How can we extend the fairness evaluation framework to other types of bias beyond the social attributes considered in this paper?

To extend the fairness evaluation framework to other types of bias beyond social attributes, we can consider incorporating additional dimensions of bias such as cultural, linguistic, or ideological biases. This can be achieved by identifying relevant attributes that are prone to bias in the context of the dataset or task at hand. For example, in a dataset related to healthcare, factors like socioeconomic status, geographical location, or access to healthcare services could be potential attributes contributing to bias.
Furthermore, we can explore the intersectionality of multiple attributes to understand how biases may compound or interact with each other. By analyzing how different attributes intersect and influence the fairness of the generated summaries, we can develop a more comprehensive framework for evaluating bias in natural language generation models.

How can we mitigate the potential biases in the training data of the LLMs that contribute to the unfairness in the generated summaries?

Mitigating biases in the training data of Language Models (LLMs) requires a multi-faceted approach:

Data Preprocessing: Conduct thorough data preprocessing to identify and address biases in the training data. This may involve data augmentation, balancing datasets, or removing biased samples.

Diverse Training Data: Ensure that the training data is diverse and representative of the population to minimize biases. Incorporate data from various sources and perspectives to reduce the impact of skewed data.

Bias Detection Algorithms: Implement bias detection algorithms to identify and quantify biases in the training data. This can help in understanding the root causes of bias and taking corrective actions.

De-biasing Techniques: Utilize de-biasing techniques such as adversarial training, counterfactual data augmentation, or fairness constraints during model training to reduce biases in the LLMs.

Regular Auditing: Continuously audit the training data and model outputs for biases. Implement feedback loops to iteratively improve the fairness of the models.

How can we design summarization models that are inherently fair, rather than relying on post-processing techniques to improve fairness?

Designing summarization models that are inherently fair involves integrating fairness considerations into every stage of the model development process:

Fairness-Aware Training: Incorporate fairness constraints and objectives into the model training process. This can involve optimizing the model to generate summaries that maintain a balanced representation of different perspectives and attributes.

Fairness by Design: Develop models that are inherently fair by design, considering fairness as a core principle during architecture design and hyperparameter tuning. This may involve incorporating fairness metrics into the loss function or model architecture.

Diverse Training Data: Train the model on diverse and representative datasets to ensure that it learns to generate fair summaries across different attributes and perspectives.

Interpretable Models: Design models that provide transparency and interpretability in their decision-making process. This can help in identifying and addressing biases during model development.

Ethical Guidelines: Establish ethical guidelines and frameworks for developing fair summarization models, ensuring that ethical considerations are prioritized throughout the model development lifecycle.