"To Retrieve or Not to Retrieve? Uncertainty Detection for Dynamic Retrieval Augmented Generation"

Dynamic retrieval, guided by uncertainty, improves efficiency in retrieval-augmented generation.

This paper enhances Retrieval Augmented Generation (RAG) efficiency by dynamically invoking retrieval only when the LLM is uncertain. This reduces unnecessary retrievals, especially in multi-hop question answering, where multiple retrievals are often needed.

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https://arxiv.org/abs/2501.09292

Original Problem 🤔:

→ Existing RAG systems mostly retrieve deterministically, leading to inefficiency.

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Solution in this Paper 💡:

→ This paper explores uncertainty detection methods to trigger retrieval dynamically.

→ It evaluates various uncertainty metrics to decide "to retrieve or not to retrieve".

→ The system generates a temporary sentence and assesses its uncertainty.

→ If uncertainty exceeds a threshold, a subquery is generated for retrieval.

→ Retrieved information is then added to the LLM context for improved generation.

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Key Insights from this Paper 💎:

→ Uncertainty detection can effectively reduce retrieval calls without significantly sacrificing accuracy.

→ Eccentricity metric balances retrieval efficiency and task performance well.

→ Simpler methods like Degree Matrix (Jaccard) minimize retrievals while maintaining reasonable performance.

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Results 📊:

→ Eccentricity achieves the highest F1 score (0.605) with fewer retrievals.

→ Degree Matrix (Jaccard) achieves an F1 score of 0.524 with the least retrievals.

→ Always Retrieve baseline achieves 0.552 F1 with significantly more retrievals.