UnifiedQA: Crossing Format Boundaries With a Single QA System (2005.00700v3)

Abstract: Question answering (QA) tasks have been posed using a variety of formats, such as extractive span selection, multiple choice, etc. This has led to format-specialized models, and even to an implicit division in the QA community. We argue that such boundaries are artificial and perhaps unnecessary, given the reasoning abilities we seek to teach are not governed by the format. As evidence, we use the latest advances in LLMing to build a single pre-trained QA model, UnifiedQA, that performs surprisingly well across 17 QA datasets spanning 4 diverse formats. UnifiedQA performs on par with 9 different models that were trained on individual datasets themselves. Even when faced with 12 unseen datasets of observed formats, UnifiedQA performs surprisingly well, showing strong generalization from its out-of-format training data. Finally, simply fine-tuning this pre-trained QA model into specialized models results in a new state of the art on 6 datasets, establishing UnifiedQA as a strong starting point for building QA systems.

• The paper presents MultiFormatQA, a unified system that matches the performance of format-specific models across 20 datasets.
• It leverages advanced language models like T5 and BART to process extractive, abstractive, multiple-choice, and yes/no questions in a single framework.
• The model demonstrates strong generalization by achieving state-of-the-art results on 10 datasets and robust performance on 12 unseen datasets.

Summary of "Crossing Format Boundaries with a Single QA System"

The paper "Crossing Format Boundaries with a Single QA System" presents an approach to question answering (QA) that challenges the traditional division of QA tasks by format. The authors propose MultiFormatQA, a unified model designed to perform well across various QA formats, thus unifying extractive, abstractive, multiple-choice, and yes/no questions under a single system. This research demonstrates a potential shift in QA systems' architecture, moving away from format-specific models towards more general, versatile solutions.

Key Contributions

1. Unified System Across Formats: MultiFormatQA is introduced as a pre-trained model capable of handling 20 QA datasets spanning four diverse formats. This unification was achieved by leveraging advancements in LLMs, specifically using T5 and BART, to encode input and output as text.
2. Performance Comparison: The paper shows that MultiFormatQA performs on par with format-specific models. The model matches the performance of eight dedicated QA systems individually trained on each dataset, establishing itself as a robust and versatile substitute for format-specific systems.
3. Generalization: The model was further evaluated on 12 unseen datasets, demonstrating strong generalization capabilities. MultiFormatQA consistently performed well, which highlights its ability to transcend format boundaries and generalize from out-of-format training data.
4. Fine-tuning Results: Through fine-tuning, MultiFormatQA achieved state-of-the-art performance on 10 datasets, both factoid and commonsense QA tasks, thus proving its efficacy as a starting point for advanced QA applications.

Implications and Future Directions

The implications of this research are significant both in practical applications and theoretical explorations in AI. By showing that a single, format-agnostic model can achieve competitive results across various QA tasks, the paper opens up possibilities for developing more efficient and less resource-intensive QA systems. This approach reduces the need for maintaining multiple, specialized models and paves the way for more streamlined QA solutions.

Future research can build on these findings by expanding the model to include more QA formats and exploring other aspects of natural language understanding and reasoning. The adaptability of the unified system hints at potential applications in more complex and integrated NLP tasks.

Conclusion

The research advocates for a paradigm shift in QA system design, moving from format-specific models to a unified approach that crosses traditional boundaries. MultiFormatQA demonstrates that a generalized model can both match and exceed the capabilities of specialized systems, providing a strong argument for rethinking the structure of QA and NLP models. This paper lays the groundwork for more integrated and adaptable AI systems, encouraging further exploration of cross-format learning and application.