GenQREnsemble and GenQREnsembleRF: Leveraging Ensemble Prompting for Effective Zero-Shot Query Reformulation

Core Concepts

Ensemble prompting with paraphrased instructions can significantly improve the performance of zero-shot query reformulation, outperforming single-instruction approaches. The proposed GenQREnsemble and GenQREnsembleRF methods leverage this insight to generate more effective reformulations, leading to substantial gains in retrieval performance across multiple benchmarks.

Abstract

The paper proposes two novel methods for query reformulation:

GenQREnsemble:

Generates multiple paraphrased instructions for the query reformulation task using an LLM.
Prompts the FlanT5 model with each of these instructions along with the original query to generate a set of reformulated queries.
Appends all the generated keywords to the original query to create the final reformulated query.
Demonstrates significant improvements in pre-retrieval settings, with up to 18% relative gains in nDCG@10 and 24% in MAP compared to the previous state-of-the-art zero-shot approach.

GenQREnsembleRF:

Extends GenQREnsemble to incorporate relevance feedback by prepending the instructions with a fixed context string describing the feedback documents.
Shows improvements in post-retrieval settings, with up to 9% relative gains in nDCG@10 using relevant feedback documents, and 5% in MRR using pseudo-relevance feedback.

The key insights are:

Ensemble prompting with paraphrased instructions can significantly enhance the performance of zero-shot query reformulation.
The proposed methods outperform both the raw queries and the previous state-of-the-art zero-shot approaches across multiple IR benchmarks.
The gains are more pronounced in the traditional sparse retrieval setting compared to the neural reranking setting, suggesting that the ensemble approach is particularly beneficial when the retriever has limited capabilities.

Stats

"Relative nDCG@10 improvements up to 18% and MAP improvements up to 24% over the previous zero-shot state-of-art."
"On the MSMarco Passage Ranking task, GenQREnsembleRF shows relative gains of 5% MRR using pseudo-relevance feedback, and 9% nDCG@10 using relevant feedback documents."

Quotes

"GenQREnsemble outperforms the raw queries as well as generates better reformulations than FlanQR's reformulated queries across all the four benchmarks over a BM25 retriever, indicating the usefulness of paraphrasing initial instructions."
"GenQREnsembleRF is able to improve retrieval performance as compared to other PRF approaches and is able to incorporate feedback from a BM25 retriever better than RM3 as well as its zero-shot counterpart."

Key Insights Distilled From

GenQREnsemble

by Kaustubh Dho... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.03746.pdf

Deeper Inquiries

How can the proposed ensemble prompting approach be extended to other natural language tasks beyond query reformulation?

The ensemble prompting approach proposed in the context can be extended to various other natural language tasks by leveraging the power of large language models (LLMs) and diverse prompts. For tasks like text generation, sentiment analysis, summarization, or question answering, ensembling multiple prompts can provide a more comprehensive view of the input data and generate diverse outputs. By paraphrasing instructions or prompts and prompting the LLM with different variations, the model can capture a broader range of linguistic patterns and generate more nuanced responses.
Ensemble prompting can also be applied to tasks requiring creativity or multiple perspectives, such as content creation, dialogue systems, or language translation. By combining outputs from multiple prompt variations, the ensemble approach can enhance the richness and diversity of generated content. Additionally, in tasks like information extraction or knowledge graph construction, ensembling prompts can help capture different facets of the input data and improve the overall quality of extracted information.
Overall, the ensemble prompting technique can be a versatile approach applicable to a wide range of natural language tasks, providing benefits in terms of diversity, robustness, and performance improvement.

What are the potential limitations or drawbacks of the ensemble prompting strategy, and how can they be addressed?

While ensemble prompting offers several advantages, there are potential limitations and drawbacks that need to be considered:

Increased Computational Cost: Ensembling multiple prompts can lead to higher computational requirements and longer inference times. This can be addressed by optimizing the ensemble strategy, using efficient parallel processing, or leveraging specialized hardware like GPUs or TPUs.

Risk of Redundancy: If the prompts in the ensemble are too similar, there is a risk of generating redundant outputs. To mitigate this, prompts should be carefully designed to provide diverse perspectives and inputs to the LLM.

Complexity in Model Integration: Integrating outputs from multiple prompt variations can be challenging, especially in complex tasks or models. Proper aggregation techniques, such as voting mechanisms or attention mechanisms, can help combine diverse outputs effectively.

Evaluation and Interpretation: Assessing the performance of an ensemble model and interpreting the results can be more complex than for individual models. Clear evaluation metrics and validation strategies are essential to measure the effectiveness of the ensemble approach.

Addressing these limitations requires careful design, experimentation, and optimization of the ensemble prompting strategy to maximize its benefits while minimizing potential drawbacks.

Could the ensemble prompting technique be combined with other query reformulation methods, such as those based on user feedback or external knowledge sources, to further enhance retrieval performance?

Yes, the ensemble prompting technique can be effectively combined with other query reformulation methods, such as those based on user feedback or external knowledge sources, to enhance retrieval performance. By integrating ensemble prompting with techniques like pseudo-relevance feedback (PRF) or feedback from user interactions, the reformulated queries can benefit from both the diversity of prompts and the relevance feedback provided by users or external sources.
For example, in the context of query reformulation, combining ensemble prompting with user feedback can help generate more personalized and contextually relevant queries. The ensemble approach can provide a diverse set of reformulations, while user feedback can guide the selection of the most relevant and effective query variants.
Similarly, incorporating external knowledge sources, such as domain-specific databases or ontologies, into the ensemble prompting process can enrich the reformulated queries with domain expertise and specific terminology. This hybrid approach can lead to more precise and contextually appropriate query reformulations, ultimately improving retrieval performance.
By synergistically combining ensemble prompting with other query reformulation methods, researchers and practitioners can leverage the strengths of each approach to create more effective and tailored solutions for information retrieval tasks.