Generative Relevance Feedback and Convergence of Adaptive Re-Ranking: Improving Passage Retrieval with Large Language Models and Corpus Graphs

Concetti Chiave

Generative relevance feedback and adaptive re-ranking can improve passage retrieval performance, with the most effective approach combining generative pseudo-relevance feedback and adaptive re-ranking over a large corpus graph.

Sintesi

The University of Glasgow Terrier team participated in the TREC 2023 Deep Learning track to explore new generative approaches to retrieval and validate existing approaches. They investigated generative query reformulation (Gen-QR) and generative pseudo-relevance feedback (Gen-PRF) using the FLAN-T5 language model, as well as conducting a deeper evaluation of adaptive re-ranking (GAR) on the MS MARCO-v2 corpus.

The team found that generative relevance feedback can transfer to a monoELECTRA cross-encoder and is further bolstered by adaptive re-ranking. They observed that while generative relevance feedback can be generally effective, the approach is sensitive to the form of the query, with performance degrading for conversational-style queries due to artifacts from instruction-tuning.

The team also found that with a sufficient compute budget and corpus graph size, a first-stage lexical model like BM25 can closely replicate the metric performance of a learned sparse retrieval model like SPLADE, with the rankings becoming increasingly correlated as the budget increases. This suggests that in cases where labeled data is unavailable or costly to collect, adaptive re-ranking can provide a compelling alternative to complex first-stage retrieval models.

The team's most effective run combined generative pseudo-relevance feedback and adaptive re-ranking, outperforming other approaches in terms of P@10 and nDCG@10. This highlights the potential of these techniques to improve passage retrieval performance.

Statistiche

Generative relevance feedback can improve directed queries with both clear and ambiguous intent.
Performance can degrade when a query is posed as a conversational-style question, as the underlying language model may attempt to answer the question directly instead of generating suitable expansion terms.
With a sufficient compute budget and corpus graph size, a first-stage lexical model like BM25 can closely replicate the metric performance of a learned sparse retrieval model like SPLADE, with the rankings becoming increasingly correlated as the budget increases.

Citazioni

"Generative relevance feedback can transfer to a monoELECTRA cross-encoder and is further bolstered by adaptive re-ranking."
"With a sufficient compute budget and corpus graph size, a first-stage lexical model like BM25 can closely replicate the metric performance of a learned sparse retrieval model like SPLADE, with the rankings becoming increasingly correlated as the budget increases."

Approfondimenti chiave tratti da

Generative Relevance Feedback and Convergence of Adaptive Re-Ranking: University of Glasgow Terrier Team at TREC DL 2023

by Andrew Parry... alle arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01122.pdf

Domande più approfondite

What other types of language models or prompting techniques could be explored to improve the performance of generative relevance feedback, especially for conversational-style queries

To enhance the performance of generative relevance feedback, particularly for conversational-style queries, exploring advanced language models and prompting techniques is crucial. One promising avenue is leveraging transformer-based models like GPT-3 or GPT-4, known for their contextual understanding and generation capabilities. These models can be fine-tuned on conversational data to better grasp the nuances of such queries and generate more relevant expansion terms. Additionally, utilizing prompting techniques that guide the language model to focus on query expansion rather than direct answers can help tailor the output to suit the needs of information retrieval tasks. By providing specific instructions or constraints during generation, the model can be directed to produce expansion terms that align more closely with the search intent of conversational queries.

How can the computational efficiency of adaptive re-ranking be further improved to make it a more practical solution for real-world retrieval systems

Improving the computational efficiency of adaptive re-ranking is essential to make it a more practical solution for real-world retrieval systems. One approach to enhance efficiency is through optimized graph traversal algorithms that reduce the time complexity of identifying nearest neighbors in the corpus graph. Techniques like graph pruning, early stopping criteria, and parallel processing can streamline the traversal process and minimize computational overhead. Moreover, implementing intelligent caching mechanisms to store intermediate results and avoid redundant computations can further boost efficiency. Additionally, exploring distributed computing frameworks and hardware acceleration techniques can help scale adaptive re-ranking to handle larger datasets and improve overall performance in real-time retrieval scenarios.

What are the implications of the finding that a simple lexical model can closely match the performance of a learned sparse retrieval model when using a large corpus graph and adaptive re-ranking, and how might this impact the future development of retrieval systems

The finding that a simple lexical model can closely match the performance of a learned sparse retrieval model with the aid of a large corpus graph and adaptive re-ranking has significant implications for the future of retrieval systems. This suggests that the reliance on complex learned models for initial ranking may not always be necessary, especially when coupled with efficient re-ranking strategies. The implications include reduced computational costs, faster retrieval speeds, and potentially more scalable systems. This finding opens up possibilities for developing lightweight retrieval pipelines that leverage graph-based re-ranking techniques to achieve comparable performance to sophisticated learned models. In practice, this could lead to more accessible and cost-effective information retrieval solutions that maintain high accuracy and relevance in diverse search scenarios.