NewMind AI Journal #84
When Speed Meets Precision: How TurkEmbed4Retrieval Challenges the Reranking Status Quo
By NewMind AI Team
📌 RAG systems typically rely on a two-stage pipeline: a fast retriever for broad document selection, followed by a slower, compute-heavy reranker for precision.
📌 Conventional wisdom assumes reranking is essential for achieving top-tier accuracy in Retrieval-Augmented Generation pipelines.
📌 We questioned this assumption by benchmarking our domain-specialized retriever against several state-of-the-art rerankers using real-world Turkish legal data.
📌 The results challenge the norm—our optimized retriever delivered a superior balance of speed, efficiency, and accuracy, often making reranking unnecessary in production-grade AI systems.
The RAG Pipeline: Understanding the Architecture
To make sense of our findings, it's important to understand the two main components of a RAG pipeline: retrieval and reranking.
Retrieval (Bi-encoders) works by independently converting both queries and documents into vectors. This allows the system to quickly compare and retrieve documents that are most similar to the query. Think of it as a super-fast librarian who already knows where every book is and can instantly guide you to the right shelf. It’s extremely fast and scales well, but the results can sometimes be approximate—good, but not perfect.
Reranking (Cross-encoders), on the other hand, processes each query and document together. This allows it to deeply analyze the relevance of each match and assign a much more accurate score. It’s like a careful scholar who reads through the top results to pick out the very best one. The downside is that it’s much slower and computationally expensive, especially as the number of documents increases.
This creates a common trade-off in RAG systems: speed versus precision. But our research challenges the assumption that reranking is always necessary. What if retrieval alone could be made accurate enough to stand on its own?
The Benchmark: Real-World Data, Rigorous Testing
To find the answer, we designed a comprehensive experiment using our open-source SIU-RAG dataset, a collection of Turkish legal and regulatory texts paired with real user questions from legal professionals. This isn't generic data; it's dense, domain-specific content reflecting the challenges of building real-world legal technology.
We tested our proprietary TurkEmbed4Retrieval (in 768 and 512-dimensional variants) against six popular cross-encoder rerankers, including models from JinaAI, Alibaba-NLP's GTE reranker, ColBERT implementations, and the powerful Qwen3 series. Our evaluation employed strict "exact-K" requirements, where systems must find the exact set of correct documents for each query, measuring both information discovery capability and ranking quality.
Evaluation Methodology
We employed comprehensive metrics capturing both completeness and precision:
All models were evaluated with consistent batch size of 8 to ensure fair computational comparisons.
The Results: Speed, Accuracy, and Specialization Power
When the numbers came in, a compelling picture emerged. While the most powerful rerankers narrowly edged out our model on pure accuracy metrics, they did so at staggering computational costs.
Our TurkEmbed4Retrieval models answered queries in just ~0.065 seconds. The most accurate competitor, Qwen3-4B, required 3.53 seconds—over 50 times slower—for marginal accuracy gains. For real-time applications, this latency is prohibitive.
Limitations and Contextual Caveats
Although our experiments demonstrate that a carefully fine-tuned bi-encoder can outperform heavyweight cross-encoders under strict latency constraints, it is essential to recognize an important boundary condition: in the SIU-RAG benchmark each query is associated with a relatively small candidate pool—on average about six documents. In such a constrained retrieval space, a second-stage reranker has far less opportunity to exercise its discriminative power, because most of the “hard work” has effectively been done by the retriever the moment the relevant documents land in that ultra-narrow top-K set.
In practical deployments where the first-stage retriever may return tens, hundreds, or even thousands of passages, reranking can still deliver a measurable boost by reshuffling borderline-relevant texts toward the top positions. Therefore, readers should interpret our results as a proof-of-concept for domains in which:
The underlying corpus is heavily curated or already filtered by upstream heuristics (e.g., statute/section pre-selection in legal workflows),
Latency budgets are extremely tight, making every millisecond count,
And the acceptable recall is achievable within a single-digit top-K window.
Outside these conditions—especially when top-K expands into the high tens or hundreds—the relative advantage of a pure bi-encoder approach will likely diminish, and adding a lightweight reranker (or at least a heuristic rescoring stage) may become justified.
Going forward, we plan to extend the benchmark by:
Scaling the candidate set size in controlled increments (8, 32, 128, 512) to map the “break-even” point where rerankers start to outperform on cost-adjusted quality metrics,
Profiling mixed architectures such as late-interaction models, which promise a middle ground between bi-encoder speed and cross-encoder fidelity,
And experimenting with adaptive retrieval depths, so the system can invoke reranking only for queries whose confidence intervals suggest higher uncertainty.
By explicitly stating this limitation, we aim to encourage nuanced adoption decisions: use a single-stage retriever when your production setting mirrors our low-K regime, but keep reranking in your toolbox for larger-scale, less controlled retrieval scenarios.
Our Mind
While powerful cross-encoder rerankers will always have their place in scenarios where accuracy is the only metric that matters, our research provides a compelling counter-narrative. By focusing on building proprietary, domain-specialized retrieval systems, we've demonstrated that achieving optimal balance of speed, cost, and accuracy is possible for real-world, scalable AI applications.
TurkEmbed4Retrieval proves that in the complex world of RAG, the fastest path to excellent answers isn't always the one with the most steps. Sometimes, it's the one with the smartest start.
Key Takeaways
Prioritize the Retriever: Treat the retrieval model as a core component—not just a first pass. Investing in domain-specialized retrievers can significantly boost overall pipeline performance and efficiency.
Question the Default Pipeline: Don’t assume rerankers are always necessary. Weigh the trade-offs carefully—minor accuracy gains may not justify major increases in latency and infrastructure costs.
Embrace Domain Adaptation: Choose models that are purpose-built and fine-tuned for your specific domain (e.g., legal, finance, healthcare) instead of relying solely on large, general-purpose models.
Consider Scalability in Production: Bi-encoder architectures scale linearly and work efficiently with nearest-neighbor search, making them far more practical for large-scale deployments than compute-heavy cross-encoders.
References
SIU-RAG Dataset: “SIU-RAG: A Turkish Legal and Regulatory Retrieval Dataset,” NewMindAI, Hugging Face. Available at: https://huggingface.co/datasets/newmindai/siu-rag-data
Kusupati, A., Bhatt, G., Rege, A., Wallingford, M., Sinha, A., Ramanujan, V., ... & Farhadi, A. (2022). Matryoshka representation learning. Advances in Neural Information Processing Systems, 35, 30233-30249. https://arxiv.org/abs/2205.13147
JinaAI Reranker: JinaAI, “jina-reranker-v2-base-multilingual,” Hugging Face. Available at: https://huggingface.co/jinaai/jina-reranker-v2-base-multilingual
GTE Multilingual Reranker: Alibaba-NLP, “gte-multilingual-reranker-base,” Hugging Face. Available at: https://huggingface.co/Alibaba-NLP/gte-multilingual-reranker-base
Reason-ModernColBERT: Reason-AI, “modern-colbert-reranker,” Hugging Face. Available at: https://huggingface.co/lightonai/Reason-ModernColBERT
YTU-CE-COSMOS Turkish ColBERT: YTU-CE-COSMOS, “turkish-colbert-reranker,” Hugging Face. Available at: https://huggingface.co/ytu-ce-cosmos/turkish-colbert
SentenceTransformers Library: Reimers, N. & Gurevych, I., “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,” EMNLP 2019.
RAG Pipeline & Metrics: Lewis, P. et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” NeurIPS 2020.