---
title: "Hybrid retrieval (TR)"
source: "https://systems-analysis.info/int/Hybrid_retrieval_(TR)"
wiki: "systems-analysis.info/int"
article: "Hybrid_retrieval_(TR)"
language: "tr"
categories:
  - "Category:Large language models"
  - "Category:Prompt engineering"
  - "Category:Turkish"
revision_id: 3099
wiki_created_at: 2026-09-06T23:15:41Z
wiki_modified_at: 2026-09-06T23:15:41Z
downloaded_at: 2026-09-07T22:55:07Z
---

# Hybrid retrieval (TR)

**Hybrid Retrieval (hibrit retrieval)** — sözlüksel (sparse) ve anlamsal (dense/late‑interaction) sinyalleri birleştirerek erişim tamlığını ve hassasiyetini artırmaya yönelik bilgi erişim yöntemleri sınıfıdır. Hibrit şemalar, tam terim eşleşmesinin (BM25/TF-IDF) ve vektör yakınlığının (bi-encoder'lar, geç etkileşimli çok vektörlü modeller) avantajlarını bir araya getirir; ayrıca farklı ölçekli skorlamaya karşı dayanıklı sıralama birleştirme yöntemlerini (örneğin Reciprocal Rank Fusion, CombSUM/CombMNZ) ve cross-encoder ile yeniden sıralamayı kullanır.<sup>[\[1\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-1)[\[2\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-2)[\[3\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-3)</sup>

## Tanım ve Motivasyon

*Hibrit retrieval* — iki (veya daha fazla) bağımsız sinyal kanalında paralel ya da kademeli arama yapılması ve ardından birleştirme ve/veya yeniden sıralama uygulanmasıdır. Tipik motivasyonlar: (i) "terminolojik boşluğun" aşılması (eş anlamlılar, yeniden ifadeler), (ii) yazım hatalarına/morfolojiye dayanıklılık, (iii) belirli kodların/tanımlayıcıların çıkarılması (sparse modelin güçlü olduğu durum), (iv) yeni alan/dillere aktarım (dense modellerin anlamsal genelleme sağladığı durum).<sup>[\[4\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-4)[\[5\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-5)[\[6\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-6)</sup>

## Hibrit Aramanın Bileşenleri

### Sözlüksel (sparse)

- **Klasik modeller.** TF-IDF ve BM25/BM25F — ters çevrilmiş indeksler üzerindeki standart temel yöntemler; BM25, olasılıksal PRF çerçevesinde gerekçelendirilmiş olup ilk aşama sıralamada yaygın biçimde kullanılmaktadır.<sup>[\[7\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-7)</sup>
- **Öğrenilebilir sparse.**
  - **SPLADE / SPLADE++/v3.** Seyreklik düzenlemesiyle MLM başlığı üzerinden terim genişletme ve ağırlıklandırmayı öğrenen bir nöro-sparse model; güçlü sonuçlar ve iyi aktarılabilirlik göstermektedir (BEIR).<sup>[\[8\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-8)[\[9\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-9)[\[10\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-10)</sup>
  - **uniCOIL/COIL.** Bağlamsal ters çevrilmiş listeler ve bunların basitleştirilmiş sürümü olan *uniCOIL*; klasik ters çevrilmiş indekslerle uyumludur.<sup>[\[11\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-11)</sup>

### Anlamsal (dense/late‑interaction)

- **Bi‑encoder (tek vektör).** Sorgu ve belge vektör modelleriyle kodlanır; benzerlik dot-product/MIPS ile hesaplanır. Örnekler: DPR,<sup>[\[12\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-12)</sup> ANCE,<sup>[\[13\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-13)</sup> Contriever,<sup>[\[14\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-14)</sup> GTR,<sup>[\[15\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-15)</sup> E5.<sup>[\[16\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-16)</sup>
- **Late‑interaction (çok vektör).** "Geç" etkileşimde token düzeyindeki karşılıkları modeller: ColBERT/ColBERTv2; daha büyük indeks/gecikme pahasına daha iyi doğruluk sağlar, mühendislik araçlarıyla hafifletilir (PLAID, WARP).<sup>[\[17\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-17)[\[18\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-18)[\[19\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-19)</sup>

## Hibritleştirme ve Sıralama Birleştirme Şemaları

- **Paralel arama ve aday birleştirme.** Sparse ve dense kanallarından kendi iç skorlarıyla bağımsız aday listeleri elde edilir; ardından sıralama birleştirme uygulanır.<sup>[\[20\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-20)</sup>
- **RRF (Reciprocal Rank Fusion).** Uyumsuz skorlamaya dayanıklı, ters sıraları toplayan teknik:

${RRF}(d) = \sum\limits_{i = 1}^{m}\frac{1}{k + {rank}_{i}(d)}$, genellikle $k \approx 60$.<sup>[\[21\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-21)</sup> Endüstriyel arama motorlarında (Elasticsearch/OpenSearch) yerleşik retriever/işlemci olarak desteklenmektedir.<sup>[\[22\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-22)[\[23\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-23)</sup>

- **CombSUM/CombMNZ ve diğerleri.** Gerektiğinde normalleştirme ile klasik "skor toplama" fonksiyonları.<sup>[\[24\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-24)[\[25\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-25)[\[26\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-26)</sup>
- **Ağırlıklı doğrusal karışım.**

$S(d) = \alpha \cdot S_{\text{sparse}}(d) + (1 - \alpha) \cdot S_{\text{dense}}(d)$, $\alpha \in \lbrack 0,1\rbrack$. $\alpha$ seçimi sabit ya da öğrenilebilir olabilir (koleksiyona/sorguya göre).<sup>[\[27\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-27)</sup>

- **Skor normalleştirme.** CombSUM/CombMNZ için ölçeklerin uyumlandırılması amacıyla çoğunlukla min‑max, z‑score vb. uygulanır;<sup>[\[28\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-28)</sup> alternatif olarak RRF yalnızca sıralara dayanır.
- **Dinamik/uyarlamalı ağırlıklandırma.** Kanal seçimi/ağırlığı için sorgu yönlendirme (query routing), sorgu özellikleri ve LTR modelleri; güncel çalışmalar, basit eğitilmiş karışımın çoğunlukla RRF'yi aştığını ve normalleştirmeye az duyarlı olduğunu göstermektedir.<sup>[\[29\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-29)</sup>

## Yeniden Sıralama ve Çok Aşamalı Pipeline'lar

Hibrit sistemler genellikle *retrieval → fusion → rerank* şeklinde kurulmaktadır. Yeniden sıralama için şunlar kullanılır:

- **Cross-encoder'lar (BERT/T5).** En doğru ancak en maliyetli yöntem: ilk N aday için MonoBERT/MonoT5.<sup>[\[30\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-30)[\[31\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-31)</sup>
- **Late‑interaction yeniden sıralayıcı olarak.** ColBERT ailesi yeniden sıralayıcı olarak da kullanılabilir; modern hızlandırıcılar (PLAID, WARP) kalite kaybı olmaksızın gecikmeyi azaltır.<sup>[\[32\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-32)[\[33\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-33)</sup>

*Kalite ↔ gecikme/maliyet* dengesi, RAG ve katı SLA koşullarında (bkz. kuyruk gecikmeleri p95/p99) özellikle önem taşımaktadır.<sup>[\[34\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-34)</sup>

## Benchmark Değerlendirmesi

- **BEIR.** Retriever'ların sıfır/alan dışı değerlendirmesi için heterojen koleksiyon/görevlerden oluşan birleşik küme (örn. TREC‑COVID, NFCorpus, NQ, HotpotQA, FiQA‑2018, DBPedia‑entity, ArguAna, Webis‑Touché‑2020, FEVER/Climate‑FEVER, Scidocs, SciFact, CQADupStack vb.).<sup>[\[35\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-35)</sup>
- **TREC Deep Learning / MS MARCO.** Büyük veri koşullarında retriever ve yeniden sıralayıcı eğitimi/değerlendirmesi için klasik kaynaklar.<sup>[\[36\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-36)[\[37\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-37)[\[38\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-38)</sup>
- **Kalite metrikleri.** nDCG@k, Recall@k, MRR; performans için — gecikme p50/p95/p99, QPS; işletim için — bellek/maliyet (CPU/GPU, indeks).<sup>[\[39\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-39)[\[40\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-40)</sup>
- **Ablasyon çalışmaları.** Her kanalın/ağırlığın katkısının, RRF'deki $k$ ve karışımdaki $\alpha$ parametrelerine duyarlılığın sabitlenmesi; yeniden ifadelere ve OOD kaymasına karşı dayanıklılığın değerlendirilmesi önerilmektedir.<sup>[\[41\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-41)[\[42\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-42)</sup>

## Mühendislik Boyutları ve Prodüksiyon Pratikleri

- **İndeksler ve ANN.** MIPS/kosinüs benzerliği için FAISS (Flat/HNSW/IVF‑PQ), HNSW, ScaNN.<sup>[\[43\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-43)[\[44\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-44)[\[45\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-45)</sup>
- **IR yığını.** Sparse/dense ve hibrit pipeline'lar için Lucene/Anserini/Pyserini; BEIR üzerinde "iki tuşla" yeniden üretilebilirlik.<sup>[\[46\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-46)[\[47\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-47)</sup>
- **Vektör veritabanları ve arama motorları.** Qdrant, Weaviate, pgvector/PostgreSQL, Vespa, Elasticsearch/OpenSearch yerel hibrit arama modlarına (BM25F+vektör) ve/veya RRF/doğrusal karışıma sahiptir.<sup>[\[48\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-48)[\[49\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-49)[\[50\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-50)[\[51\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-51)[\[52\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-52)</sup>
- **RAG pattern'i.** Mimari: **retrieval → fusion → rerank → LLM bağlamı** ve token sınırlama ile kaynak izleme.<sup>[\[53\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-53)</sup>
- **İndeks güncelleme, tekilleştirme, tokenizasyon.** BM25 ile vektörleştirici arasında tokenizasyonun uyumlandırılması; karıştırma öncesinde skor kalibrasyonu (normalleştirme/ölçekleme) önem taşımaktadır.<sup>[\[54\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-54)</sup>

## Kısıtlamalar ve Açık Sorular

- **Aktarılabilirlik ve çok dillilik.** Dense modeller (GTR/E5) aktarımı iyileştirir, ancak alana/dile duyarlıdır; sparse modeller (SPLADE) OOD koşullarında genellikle daha dayanıklıdır.<sup>[\[55\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-55)[\[56\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-56)</sup>
- **LLM ile entegrasyon ve halüsinasyonlar.** Hibrit retrieval, RAG bağlamlarındaki eksiklikleri ve gürültüyü azaltır, ancak halüsinasyonları tam olarak ortadan kaldırmaz; güçlü yeniden sıralayıcılar ve kaynak filtreleme gerekmektedir.<sup>[\[57\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-57)</sup>
- **Maliyet ve gizlilik.** Çok vektörlü indeks depolama, sıkıştırma, şifreleme ve yerinde (on-prem) yığın; TCO değerlendirmesi.
- **Eğilimler.** Belge/sorgu genişletme olarak HyDE/doc2query/PRF;<sup>[\[58\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-58)[\[59\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-59)</sup> karışım eğitimi (sorgu başına $\alpha$), daha verimli late-interaction (PLAID/WARP), uzun belgeler ve çok vektörlü indeksler.<sup>[\[60\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-60)[\[61\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-61)</sup>

## Yöntemlerin Karşılaştırma Tablosu

2025‑09‑10 itibarıyla (BEIR *trec‑covid* koleksiyonu örneği; nDCG@10 / Recall@100):<sup>[\[62\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-62)</sup>

| Yöntem                         | Tür (sparse/dense/hybrid) | Fikir/model                             | Birleştirme şeması                | Yeniden sıralayıcı           | nDCG@10 / R@100                                           | Gecikme (göreli) | Kaynaklar                                                                                                                                                                                                                                             |
|--------------------------------|---------------------------|-----------------------------------------|-----------------------------------|------------------------------|-----------------------------------------------------------|------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| BM25                           | sparse                    | Tam terim eşleşmesi (PRF/BM25)          | —                                 | —                            | 0.595 / 0.109                                             | çok düşük        | <sup>[\[63\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-63)[\[64\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-64)</sup>                                                                               |
| SPLADE++ (ED)                  | sparse (learned)          | Seyrek terim genişletme/ağırlıklandırma | —                                 | —                            | 0.727 / 0.128                                             | düşük–orta       | <sup>[\[65\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-65)[\[66\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-66)</sup>                                                                               |
| Contriever (MS MARCO FT)       | dense                     | Karşıtlıklı öğrenme bi-encoder'ı        | —                                 | —                            | 0.596 / 0.091                                             | orta             | <sup>[\[67\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-67)[\[68\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-68)</sup>                                                                               |
| BGE‑base‑en‑v1.5               | dense                     | Güçlü evrensel embedding modeli         | —                                 | —                            | 0.781 / 0.141                                             | orta             | <sup>[\[69\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-69)</sup>                                                                                                                                                             |
| Cohere embed‑english‑v3.0      | dense                     | Endüstriyel metin embedding modeli      | —                                 | —                            | 0.818 / 0.159                                             | orta             | <sup>[\[70\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-70)</sup>                                                                                                                                                             |
| BM25 + dense (örnek: BM25+BGE) | hybrid                    | Paralel retrieval + liste birleştirme   | RRF (k≈60) veya ağırlıklı karışım | isteğe bağlı: MonoT5/ColBERT | (uygulamaya göre değişir; genellikle \> en iyi tek kanal) | orta             | <sup>[\[71\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-71)[\[72\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-72)[\[73\]](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_note-73)</sup> |

*trec‑covid* üzerinde yöntem karşılaştırması

Not: Son satır şemayı örneklendirmektedir; kesin sayılar embedding seçimine, normalleştirmeye ve birleştirme parametrelerine bağlıdır (kaynaklara ve Pyserini ile yeniden üretilebilir betiklere bakınız).

## Dış bağlantılar

- Pyserini / Anserini: github.com/castorini/pyserini • github.com/castorini/anserini
- FAISS: arXiv:1702.08734
- Weaviate (Hybrid search): docs.weaviate.io/weaviate/search/hybrid
- pgvector: github.com/pgvector/pgvector
- Vespa (Hybrid search tutorial): docs.vespa.ai/en/tutorials/hybrid-search.html

## Kaynakça

- Manning, C.D., Raghavan, P., Schütze, H. (2008). *Introduction to Information Retrieval*. Cambridge University Press. ISBN 978‑0521865715.
- Robertson, S., Zaragoza, H. (2009). *The Probabilistic Relevance Framework: BM25 and Beyond*. Foundations and Trends in Information Retrieval 3(4):333–389. DOI:10.1561/1500000019.
- Lin, J. et al. (2021). *Pyserini: A Python Toolkit for Reproducible IR*. SIGIR.
- Järvelин, K., Kekäläinen, J. (2002). *Cumulated Gain‑Based Evaluation of IR Techniques*. Information Retrieval 6:241–256. DOI:10.1023/A:1016043826386.
- Dean, J., Barroso, L.A. (2013). *The Tail at Scale*. CACM 56(2):74–80. DOI:10.1145/2408776.2408794.

## Notlar

1.  <span id="cite_note-1">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-1) Robertson, S., Zaragoza, H. (2009). *The Probabilistic Relevance Framework: BM25 and Beyond*. Foundations and Trends in Information Retrieval, 3(4), 333–389. DOI:10.1561/1500000019.</span>
2.  <span id="cite_note-2">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-2) Cormack, G.V., Clarke, C.L.A., Büttcher, S. (2009). *Reciprocal Rank Fusion Outperforms Condorcet and Individual Rank Learning Methods*. SIGIR 2009, 758–759. <a href="https://cormack.uwaterloo.ca/cormacksigir09-rrf.pdf" class="external text" rel="nofollow">PDF</a>.</span>
3.  <span id="cite_note-3">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-3) Bruch, S., Gai, S., Ingber, A. (2023). *An Analysis of Fusion Functions for Hybrid Retrieval*. ACM TOIS 42(1):1–35. DOI:10.1145/3596512 • arXiv:2210.11934.</span>
4.  <span id="cite_note-4">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-4) Manning, C.D., Raghavan, P., Schütze, H. (2008). *Introduction to Information Retrieval*. Cambridge University Press. ISBN 978‑0521865715 (см. главы о TF‑IDF, оценке и проблеме *vocabulary mismatch*).</span>
5.  <span id="cite_note-5">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-5) Izacard, G. et al. (2022). *Unsupervised Dense Information Retrieval with Contrastive Learning (Contriever)*. TACL 10:1089–1108. arXiv:2112.09118.</span>
6.  <span id="cite_note-6">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-6) Wang, L. et al. (2022/2024). *Text Embeddings by Weakly‑Supervised Contrastive Pre‑training (E5)*. arXiv:2212.03533.</span>
7.  <span id="cite_note-7">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-7) Robertson, S., Zaragoza, H. (2009). *The Probabilistic Relevance Framework: BM25 and Beyond*. DOI:10.1561/1500000019.</span>
8.  <span id="cite_note-8">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-8) Formal, T., Piwowarski, B., Clinchant, S. (2021). *SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking*. arXiv:2107.05720.</span>
9.  <span id="cite_note-9">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-9) Formal, T. et al. (2022). *Making Sparse Neural IR Models More Effective*. Findings of EMNLP. arXiv:2205.04733.</span>
10. <span id="cite_note-10">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-10) Formal, T. et al. (2024). *SPLADE‑v3: New baselines for SPLADE*. arXiv:2403.06789.</span>
11. <span id="cite_note-11">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-11) Lin, J., Ma, X. (2021). *A Few Brief Notes on DeepImpact, COIL, and uniCOIL*. arXiv:2106.14807.</span>
12. <span id="cite_note-12">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-12) Karpukhin, V. et al. (2020). *Dense Passage Retrieval for Open‑Domain QA*. EMNLP. arXiv:2004.04906.</span>
13. <span id="cite_note-13">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-13) Xiong, L. et al. (2021). *Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval (ANCE)*. ICLR. arXiv:2007.00808.</span>
14. <span id="cite_note-14">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-14) Izacard, G. et al. (2022). TACL. arXiv:2112.09118.</span>
15. <span id="cite_note-15">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-15) Ni, J. et al. (2021/2022). *Large Dual Encoders Are Generalizable Retrievers (GTR)*. EMNLP. arXiv:2112.07899.</span>
16. <span id="cite_note-16">[↑](https://systems-analysis.info/int/Hybrid_retrieval_(TR)#cite_ref-16) Wang, L. et al. (2022/2024). arXiv:2212.03533.</span>
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