RAG for Developers: How Does the Filter-Reranker Paradigm Optimize RAG Systems for Accurate Document Retrieval?

Discover why combining small and large language models in the filter-reranker paradigm enhances RAG efficiency and accuracy. Learn how SLMs filter documents and LLMs rerank hard samples.

Question

You apply the filter-reranker paradigm to narrow down your retrieved documents. After retrieval, you use a small language model (SLM) to identify and discard irrelevant tokens. What do you do next?

A. Use extreme gradient boosting to rerank the sample that the SLM returns.
B. Use the available SLM one more time to rerank its final returned samples.
C. Use a secondary SLM to rerank the samples that the primary SLM return.
D. Use a large language model to rerank the samples that the SLM return.

Answer

D. Use a large language model to rerank the samples that the SLM return.

Explanation

In the filter-reranker paradigm for Retrieval Augmented Generation (RAG), after a small language model (SLM) filters out irrelevant tokens or documents, the next step is to use a large language model (LLM) to rerank the remaining samples (Option D). This approach leverages the complementary strengths of SLMs and LLMs:

Initial Filtering with SLMs

SLMs efficiently process large volumes of data and identify “hard samples” (complex or ambiguous cases) while discarding clearly irrelevant tokens.

For example, an SLM might flag documents with conflicting context or low confidence scores for further analysis.

Reranking with LLMs

The filtered subset of challenging samples is passed to an LLM, which evaluates semantic relevance more deeply. LLMs excel at contextual understanding and refining rankings based on nuanced query-document relationships.

This two-stage process balances speed (via SLMs) and accuracy (via LLMs), addressing the “alignment tax” seen in SLMs while avoiding the computational cost of using LLMs for all documents.

Why Other Options Fail

A (XGBoost): Traditional ML methods like XGBoost lack the contextual reasoning required for dynamic reranking in RAG.

B/C (SLM-only reranking): SLMs struggle with hard samples they initially flagged, and secondary SLMs provide minimal gains compared to LLMs.

This paradigm, validated in studies like Ma et al. (2023), improves RAG systems’ precision by 10–15% in tasks like entity recognition and relation extraction510. For developers, integrating an LLM reranker (e.g., GPT-4, CohereRerank) ensures high-quality context for LLM generation, reducing hallucinations and errors.

# Example workflow in a RAG pipeline
filtered_docs = slm_filter(retrieved_docs) # SLM filters easy samples
hard_samples = filter_by_confidence(filtered_docs)
reranked_docs = llm_reranker(hard_samples) # LLM reranks hard samples

Retrieval Augmented Generation (RAG) for Developers skill assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Retrieval Augmented Generation (RAG) for Developers exam and earn Retrieval Augmented Generation (RAG) for Developers certification.