Self-Prompting Large Language Models for Generating Pseudo QA Pairs to Improve Zero-Shot Open-Domain Question Answering

Core Concepts

This paper proposes a Self-Prompting framework that leverages the capabilities of large language models (LLMs) to automatically generate high-quality pseudo QA pairs, which are then used for in-context learning to significantly improve zero-shot open-domain question answering performance.

Abstract

The paper focuses on the task of open-domain question answering (ODQA) in a zero-shot setting, where no training data is available. The authors propose a Self-Prompting framework to address this challenge:

Passage Generation: The LLM is prompted to generate short Wikipedia-style passages on various topics, from which named entities are extracted as candidate answers.
Question Generation: The LLM is then prompted to generate questions for the extracted answers based on the passages.
Explanation Generation: Finally, the LLM is asked to provide a short explanation for each generated QA pair.

The resulting pseudo QA dataset is used for in-context learning during inference. The authors propose a clustering-based retrieval method to select diverse and semantically similar examples from the generated pool as demonstrations for each test question.
Experiments on three ODQA benchmarks (WebQ, NQ, TriviaQA) show that the proposed Self-Prompting method significantly outperforms direct prompting baselines and the previous state-of-the-art zero-shot method, and even achieves comparable performance with various customized fine-tuned models on full training data.
The authors also conduct extensive analyses on the effects of different formats for in-context learning, the quality of the generated pseudo data, and the generalization of Self-Prompting to LLMs of different sizes.

Stats

The Battle of Gallipoli was fought along the Dardanelles, a waterway that connects the Aegean Sea to the Sea of Marmara.
Splatoon is a third-person shooter video game developed and published by Nintendo for the Wii U.
The Battle of Actium took place in 31 BC, and was the decisive battle of the Final War of the Roman Republic.

Quotes

"Self-Prompting the LLM to explicitly activate different capabilities of LLMs and combine these capabilities to improve performance."
"We propose a clustering-based retrieving method to effectively utilize the built pseudo QA dataset to select both semantically similar and diverse examples for each test sample."
"Our method significantly surpasses the direct prompting baselines and the previous state-of-the-art (SOTA) zero-shot method GENREAD, and is even comparable with strong few-shot methods."

Key Insights Distilled From

Self-Prompting Large Language Models for Zero-Shot Open-Domain QA

by Junlong Li,J... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2212.08635.pdf

Deeper Inquiries

How can the Self-Prompting framework be extended to other language understanding tasks beyond open-domain question answering?

The Self-Prompting framework can be extended to various language understanding tasks beyond open-domain question answering by adapting the generation process to suit the specific requirements of the task at hand. Here are some ways to extend the framework:

Task-specific Prompting: Tailor the prompts given to the LLM to generate data relevant to the specific task. For tasks like sentiment analysis, summarization, or language translation, prompts can be designed to elicit the necessary information for the task.

Data Augmentation: Use Self-Prompting to generate additional training data for supervised learning tasks. By creating synthetic data, the model can be exposed to a more diverse range of examples, potentially improving its performance.

Multi-turn Dialogue: For tasks involving multi-turn dialogue or conversation modeling, prompts can be designed to generate coherent dialogues between multiple speakers, enabling the model to understand and respond appropriately.

Knowledge Graph Construction: Self-Prompting can be used to generate triples for knowledge graph construction tasks. By prompting the LLM to create subject-predicate-object triples, it can assist in building structured knowledge bases.

Text Generation: Extend the framework for tasks like text generation, where the model needs to create coherent and contextually relevant text. Prompts can guide the LLM to generate paragraphs, stories, or essays.

By customizing the prompts and generation process, the Self-Prompting framework can be adapted to a wide range of language understanding tasks, providing a flexible and versatile approach to leveraging large language models.

How can the quality and diversity of the generated pseudo QA pairs be further improved to better support in-context learning?

To enhance the quality and diversity of the generated pseudo QA pairs for better support in-context learning, several strategies can be implemented:

Fine-tuning Prompt Templates: Refine the prompt templates used for generating passages, questions, answers, and explanations to ensure they are clear, concise, and relevant. Iteratively optimize the prompts based on the model's performance and feedback.

Diverse Topic Selection: Expand the range of topics used for generating passages to cover a broader spectrum of domains and subjects. This can help in creating a more diverse dataset that challenges the model across different contexts.

Named Entity Variation: Introduce variations in the named entities extracted from passages to avoid repetition and enhance the model's ability to generalize to new entities. This can prevent the model from memorizing specific entities and encourage more robust learning.

Explanation Complexity: Encourage the LLM to generate more detailed and informative explanations for the QA pairs. This can help in providing additional context and reasoning for the answers, leading to a deeper understanding of the content.

Quality Control Mechanisms: Implement quality control measures to filter out low-quality or inaccurate QA pairs generated by the LLM. This can involve manual review, automated checks for factual accuracy, and validation against external sources.

By incorporating these strategies, the quality and diversity of the generated pseudo QA pairs can be enhanced, leading to more effective in-context learning and improved performance of the model in language understanding tasks.

What are the potential risks and mitigation strategies for large-scale generation of synthetic data using language models?

The large-scale generation of synthetic data using language models poses several risks that need to be addressed. Here are some potential risks and mitigation strategies:

Bias and Fairness: Risk: Generated data may reflect biases present in the training data, leading to biased models. Mitigation: Implement bias detection mechanisms, diverse dataset sampling, and bias mitigation techniques to ensure fairness and reduce bias in the generated data.

Toxicity and Harmful Content: Risk: Language models may inadvertently generate toxic or harmful content. Mitigation: Employ content moderation tools, toxicity detection algorithms, and human review processes to filter out harmful content and ensure the safety of the generated data.

Privacy Concerns: Risk: Generated data may inadvertently contain sensitive or personally identifiable information. Mitigation: Implement data anonymization techniques, adhere to data privacy regulations, and conduct thorough privacy impact assessments to protect user privacy.

Quality Control: Risk: Inaccurate or low-quality data generated by the language model. Mitigation: Establish robust quality control measures, including human validation, automated checks, and validation against ground truth data to ensure the accuracy and reliability of the generated data.

Ethical Use: Risk: Misuse of synthetic data for unethical purposes. Mitigation: Develop clear guidelines and ethical frameworks for the generation and use of synthetic data, promote transparency in data generation processes, and ensure compliance with ethical standards and regulations.

Model Drift: Risk: Language models may exhibit drift over time, leading to changes in the quality or characteristics of the generated data. Mitigation: Regularly monitor model performance, retrain models on updated data, and implement mechanisms to detect and address model drift to maintain data quality.

By proactively addressing these risks and implementing appropriate mitigation strategies, the large-scale generation of synthetic data using language models can be conducted responsibly and ethically, ensuring the integrity and reliability of the generated data.