Improving Generalization of Strong Cross-Encoder Rankers through Effective Query Expansion

Core Concepts

It is possible to improve the generalization of strong neural rankers, such as RankT5 and MonoT5, by leveraging prompt engineering and aggregating the ranking results of each expanded query via fusion.

Abstract

The paper examines the impact of query expansion on the generalization of strong cross-encoder rankers, which has been an under-explored area. The authors first apply popular query expansion methods, such as RM3 and generative techniques, to state-of-the-art cross-encoder rankers and observe a deterioration in their zero-shot performance. The authors identify two key steps for successful query expansion with cross-encoders: high-quality keyword generation and minimal-disruptive query modification. They propose a framework that leverages an instruction-following language model to generate high-quality keywords through a reasoning chain, and then combines the ranking results of each expanded query dynamically using self-consistency and reciprocal rank weighting. Experiments on the BEIR and TREC Deep Learning 2019/2020 benchmarks show that this approach can improve the nDCG@10 scores of both MonoT5 and RankT5, pointing to a promising direction for applying query expansion to strong cross-encoder rankers.

Stats

The nDCG@10 score of RankT5 on TREC DL 2019 improved from 0.737 to 0.751 using the proposed method. The nDCG@10 score of MonoT5 on TREC DL 2019 improved from 0.695 to 0.724 using the proposed method. The nDCG@10 score of RankT5 on the BEIR Wiki+News dataset improved from 0.557 to 0.570 using the proposed method. The nDCG@10 score of MonoT5 on the BEIR Wiki+News dataset improved from 0.519 to 0.532 using the proposed method.

Quotes

"Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?" "It is possible to improve the generalization of a strong neural ranker, by prompt engineering and aggregating the ranking results of each expanded query via fusion."

Key Insights Distilled From

Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?

by Minghan Li,H... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2311.09175.pdf

Deeper Inquiries

How can the proposed query expansion framework be extended to other types of language models beyond cross-encoder rankers?

The proposed query expansion framework can be extended to other types of language models by adapting the keyword generation and fusion steps to suit the specific characteristics of the models. For instance, for models that are not cross-encoder rankers but still rely on token-level interactions, the keyword generation process can be modified to generate relevant keywords that align with the model's architecture and objectives. Additionally, the fusion step can be adjusted to incorporate the ranking results of expanded queries in a way that complements the model's scoring mechanism. By customizing these components based on the requirements and capabilities of different language models, the query expansion framework can be effectively applied to a variety of models beyond cross-encoder rankers.

What are the potential limitations or drawbacks of the self-consistency and reciprocal rank weighting techniques used for fusion?

While self-consistency and reciprocal rank weighting are effective techniques for fusion in the context of query expansion, they may have some limitations and drawbacks. One potential limitation is the sensitivity of these techniques to the quality of the generated keywords. If the keywords are noisy or irrelevant, the fusion process may amplify the impact of these inaccuracies, leading to suboptimal results. Additionally, the scalability of these techniques may be a concern when dealing with a large number of expanded queries, as the computational overhead of calculating reciprocal ranks and maintaining self-consistency could become significant. Moreover, the performance of these techniques may vary depending on the dataset and the specific characteristics of the language model being used, making them less universally applicable in all scenarios.

Could the keyword generation process be further improved by incorporating additional signals or techniques beyond the reasoning chain approach?

Yes, the keyword generation process could be enhanced by incorporating additional signals or techniques beyond the reasoning chain approach. One potential improvement could involve leveraging domain-specific knowledge or external resources to guide the keyword generation process. For example, incorporating domain-specific ontologies, knowledge graphs, or external databases could help generate more relevant and informative keywords. Furthermore, integrating context-awareness into the keyword generation process, such as considering the context of the query or the document being ranked, could improve the quality of the generated keywords. Additionally, exploring advanced natural language processing techniques, such as semantic similarity measures or topic modeling, could provide additional signals for generating keywords that capture the essence of the query more effectively. By integrating these additional signals and techniques, the keyword generation process can be further refined to enhance the overall performance of the query expansion framework.