Generation-Augmented Retrieval for Open-domain Question Answering (2009.08553v4)

Abstract: We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. We demonstrate that the generated contexts substantially enrich the semantics of the queries and GAR with sparse representations (BM25) achieves comparable or better performance than state-of-the-art dense retrieval methods such as DPR. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. Moreover, as sparse and dense representations are often complementary, GAR can be easily combined with DPR to achieve even better performance. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.

A Technical Review of Generation-Augmented Retrieval for Open-Domain Question Answering

The paper "Generation-Augmented Retrieval for Open-Domain Question Answering" introduces an innovative approach to improving retrieval performance in Open-Domain Question Answering Systems (OpenQA). In this context, the retrieval of relevant documents is critical, as it determines the efficacy of subsequent question-answering processes. The proposed method, Generation-Augmented Retrieval (Gar), combines sparse retrieval with generation-based techniques to enhance the quality of document retrieval.

Core Contributions and Methodology

The authors address key limitations of sparse retrieval techniques like BM25, which excel in efficiency but fail in semantic matching, and dense retrieval methods like DPR, which are computationally intensive. The proposed Gar framework augments traditional retrieval by generating relevant contexts using pre-trained LLMs (PLMs) without the need for external supervision or complex training processes.

Gar utilizes various heuristically discovered contexts generated from the original query. These generated contexts aim to embed semantic enrichment by leveraging inherent knowledge latent in PLMs. Specifically, Gar employs:

• Direct query augmentation with answers.
• Generation of sentences containing answers.
• Title generation for passages that include the answers.

This multi-contextual generation strategy is critical, as the results show that combining retrieval from multiple context-augmented queries yields significantly superior retrieval accuracy.

Numerical Results and Observations

The paper reports strong numerical results, demonstrating that Gar, when integrated with BM25, achieves retrieval performance that rivals or surpasses state-of-the-art dense retrieval methods, such as DPR. Notably, Gar performs exceptionally well in instances requiring retrieval across semantically related but non-lexically similar documents, indicative of its robust semantic understanding capabilities.

In a controlled evaluation on the Natural Questions (NQ) and TriviaQA datasets, Gar achieves notable retrieval accuracy improvements. For instance, Gar achieves a top-k retrieval accuracy on par with dense representations while maintaining the lightweight and efficient nature of sparse methods.

The paper also highlights that Gar's integration with DPR further boosts retrieval accuracy, showcasing the complementary nature of sparse and dense methods when enhanced with generative capabilities.

Theoretical and Practical Implications

The research implies significant theoretical advances by demonstrating that language generation models can augment traditional retrieval systems without additional annotated data. This highlights a potential avenue where generative models can be applied to improve retrieval tasks in various domains, bypassing previously established limitations of sparse and dense paradigms.

Practically, Gar fosters a paradigm that reduces computational overhead while improving performance, which is vital for applications where time and resource constraints are prevalent. This holds promise for real-world QA systems looking to operate efficiently across large datasets like Wikipedia.

Speculations and Future Directions

The exploration posits several promising future research directions. For instance, there is potential in optimizing the interaction between generated contexts and retrieval mechanisms. There's also scope for investigating the application of Gar in more diverse tasks beyond OpenQA, such as conversational AI and general document retrieval.

Moreover, extending Gar to exploit advanced generative models or integrating dynamic context generation based on query-conversation history or other signals could yield further improvements. Future studies might explore how fine-tuning procedures or multi-task learning frameworks could enhance generative capabilities, providing even richer contextual augmentations.

In conclusion, the introduction of Gar offers a promising advancement in OpenQA retrieval strategies, demonstrating the potential for generative models to significantly enhance traditional retrieval tasks through effective semantic augmentation. The findings present a compelling case for the integration of language generation with information retrieval systems, marking an exciting frontier for future research and application.