Corpus-Steered Query Expansion Enhancing Information Retrieval Systems with Large Language Models

Core Concepts

The author introduces Corpus-Steered Query Expansion (CSQE) to address limitations in query expansions generated by Large Language Models (LLMs). CSQE leverages the relevance assessing capability of LLMs to incorporate knowledge from the retrieval corpus, improving relevance prediction.

Abstract

Corpus-Steered Query Expansion (CSQE) enhances information retrieval systems by combining LLM-knowledge empowered expansions with corpus-originated texts. CSQE outperforms existing methods without requiring training, showcasing its effectiveness across various datasets. The approach balances out limitations associated with intrinsic knowledge of LLMs and improves retrieval performance significantly.

Stats

Recent studies show that query expansions by LLMs can boost retrieval effectiveness.
CSQE exhibits strong performance without training.
CSQE combined with BM25 outperforms SOTA models.
CSQE is robust to domain shifts and improves BM25 on all datasets.

Quotes

"CSQE utilizes the relevance assessing capability of LLMs to systematically identify pivotal sentences in initially-retrieved documents."
"CSQE balances out limitations commonly found in LLM-knowledge empowered expansions."
"CSQE demonstrates superiority over existing expansion methods across various datasets."

Key Insights Distilled From

Corpus-Steered Query Expansion with Large Language Models

by Yibin Lei,Yu... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18031.pdf

Deeper Inquiries

How can the computational overhead of using OpenAI LLMs be mitigated in practical applications?

In practical applications, the computational overhead of using OpenAI LLMs can be mitigated through several strategies:

Batch Processing: Instead of making individual API calls for each query expansion, batch processing can be implemented to optimize resource usage and reduce latency.
Caching: Caching previously generated expansions can help avoid redundant API calls for similar queries, improving efficiency.
Parallelization: Utilizing parallel computing techniques can distribute the workload across multiple processors or machines, speeding up the process of generating expansions.
Model Optimization: Fine-tuning models specifically for query expansion tasks or utilizing smaller versions of LLMs with reduced complexity can help decrease computation time.

What potential biases or limitations may arise from relying on closed-source models like GPT-3.5-Turbo?

Relying on closed-source models like GPT-3.5-Turbo may introduce several biases and limitations:

Bias in Training Data: Closed-source models are trained on proprietary datasets that might contain inherent biases present in the data, leading to biased outputs during inference.
Lack of Transparency: The inner workings and training data of closed-source models are not transparent, making it challenging to understand how decisions are made and potentially introducing opacity into the system.
Limited Customization: Users have limited control over fine-tuning or modifying closed-source models according to specific requirements or domain-specific nuances.
Dependency Risk: Dependence on a single closed-source model provider creates a risk if access is restricted or discontinued, impacting continuity and flexibility in operations.

How might the incorporation of corpus-originated texts impact the generalizability of CSQE beyond the datasets evaluated?

The incorporation of corpus-originated texts in CSQE has implications for its generalizability beyond evaluated datasets:

Enhanced Relevance - By incorporating relevant sentences directly from retrieved documents within a corpus, CSQE ensures that expanded queries maintain contextual relevance specific to each dataset's content domain.
Reduced Hallucination - Corpus-originated texts provide grounded information from existing documents, reducing reliance solely on generative capabilities that may lead to hallucinations when expanding queries with hypothetical content.
Domain Adaptation - Leveraging text directly from corpora allows CSQE to adapt more effectively across diverse domains by grounding expansions in real-world knowledge present within specific document collections used for retrieval tasks.
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