Improving Query Rewriting with Ranking Feedback

As language models grow in power, they are used in many applications, such as answering questions from large amounts of information. One technique that helps these systems is called Query Rewriting. This method changes a user's original question into a different version that is better suited for retrieving useful documents. This article discusses a new approach to improve query rewriting without the need for labeled data.

#The Role of Query Rewriting

Query rewriting is essential for systems that answer questions because the original question may not always lead to the most useful results. By rewriting the question, we can help the system find more relevant documents, which leads to better answers. Traditional methods often rely on large models, which can be expensive and slow. Therefore, smaller, more efficient models are preferred in many cases.

#Challenges in Query Rewriting

Current query rewriting methods typically require labeled data or predefined rewards for Feedback. This means that they rely on having documents marked as relevant or answers identified ahead of time, which can be time-consuming and impractical. Lack of generalization in these methods can lead to poor performance when the system encounters new types of questions or documents.

#Proposed Method: Ranking Feedback for Query Rewriting (RaFe)

To overcome these challenges, we introduce a new framework called Ranking Feedback improves Query Rewriting (RaFe). This framework trains query rewriting models without needing labeled data. Instead, it uses feedback from a reranking system, which assesses the relevance of retrieved documents. This method streamlines training and enhances the model's ability to rewrite queries more effectively.

#How RaFe Works

RaFe has a two-step process.

1. Initial Training: The first step involves training a basic query rewriting model with standard supervised learning techniques. During this stage, the model learns a variety of rewriting styles based on an initial set of data.

2. Feedback Training: After the initial training, a reranker is used to provide feedback on the rewritten queries. This reranker scores the documents retrieved using the rewritten queries and offers insights on which rewrites are effective and which are not. The feedback is used to further train the query rewriting model.

This method allows for both offline and online training.

• Offline Training: In this approach, the model uses past data to identify good and bad rewrites based on their performance in retrieving relevant documents.

• Online Training: This method scores queries in real-time and uses the results to improve the model immediately.

#Evaluation of RaFe

To test the effectiveness of RaFe, experiments were designed to evaluate its performance in real-world question answering tasks. The experiments focused on how well RaFe could rewrite queries to improve information retrieval in both English and Chinese datasets.

#Datasets Used

Various open-domain question-answering datasets were used for evaluation. In English, datasets like Natural Questions (NQ), TriviaQA, and HotpotQA served as benchmarks. For Chinese, WebQA and FreshQA were utilized. Each dataset was carefully chosen to ensure that the results could accurately reflect the system's capabilities across different languages and types of inquiries.

#Results and Findings

The results showed that RaFe outperformed existing query rewriting methods in nearly all scenarios. In particular, it demonstrated significant improvements in the settings where the aim was to expand on the original query's retrieval.

• Substitute Setting: In this setup, the system used documents retrieved by the rewritten query directly without further processing. RaFe still provided slight improvements over older methods.

• Expand Setting: When combining both the original query and its rewrites for document retrieval, RaFe achieved marked enhancements, significantly surpassing other methods.

#Analysis of Performance

A closer look at the performance in various settings highlighted how feedback-driven adjustments could refine query rewrites. It was noted that applying ranking feedback helped the model maintain the original question's meaning while improving clarity and relevance.

#Real-world Applications

The approach of using ranking feedback in query rewriting has practical implications. By making querying systems more efficient, it can lead to faster and more accurate information retrieval. This can benefit various applications, including search engines, customer support bots, and any platform requiring interactive question answering.

#Conclusion

RaFe offers a promising direction for improving query rewriting without the burden of expensive labeled data. By leveraging the scoring capabilities of rerankers, this approach paves the way for more adaptable and efficient information retrieval systems. As the research advances, integrating the rankings and rewrites into training could further enhance performance, making systems even more capable in tackling diverse queries across different languages and contexts.

#Future Directions

Looking forward, several avenues for improvement and exploration are anticipated:

1. Cross-domain Validation: Testing the model in different domains could reveal how well it adapts and performs in various contexts.

2. Joint Training: Combining the training of the reranker and the rewrite model could lead to better overall performance.

3. Exploration of Diverse Feedback Mechanisms: Investigating additional sources of feedback could enhance the rewriting process and further refine the results.

By continuing to evolve the methods used in query rewriting, the potential for more effective language models in various applications remains vast.