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QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation

Paper · 2026 · Dehai Min, Kailin Zhang, Tongtong Wu et al.

Summary

Dynamic RAG systems decide during generation when to fetch external evidence. Most rely on model-internal signals such as token probabilities, entropy or attention, but LLMs are poorly calibrated and can be confidently wrong. QuCo-RAG replaces these signals with counts taken from the model's pre-training corpus. Before answering, it checks how often the question's entities appear; rare entities point to long-tail knowledge the model likely lacks, so retrieval runs up front. While generating, it extracts head–relation–tail triplets from each sentence and checks whether the head and tail entities ever co-occur in the corpus. If they never do, the sentence is treated as a likely hallucination and is regenerated with retrieved documents. Corpus lookups use Infini-gram and return within milliseconds. On multi-hop QA with OLMo-2, whose corpus is public, the method beats strong dynamic RAG baselines. The gains carry over to models with undisclosed training data, as well as to long-form and biomedical tasks. This suggests that corpus-grounded checks can serve as a largely model-agnostic retrieval trigger.

Key points

  • The method has two stages, each with a default threshold:
    • Pre-generation entity frequency check: retrieval is triggered when the average frequency of the question's entities falls below τ_entity = 10³. Results stay stable for thresholds from 10³ to 10⁷.
    • Runtime co-occurrence check: a sentence is flagged when its head and tail entities co-occur fewer than τ_cooc = 1 times.
  • Statistics are computed with Infini-gram over OLMo-2's roughly 4-trillion-token pre-training corpus. The authors evaluate OLMo-2 at 7B, 13B and 32B scale.
  • On multi-hop QA benchmarks (e.g., 2WikiMultihopQA), QuCo-RAG improves EM by 5–12 points over state-of-the-art baselines such as DRAGIN and FLARE. Its efficiency remains competitive.
  • OLMo-2 corpus statistics also work as a proxy for Llama-3, Qwen2.5 and GPT-4.1/5-chat, raising EM by up to 14 points. QuCo-RAG also scores best on ASQA (long-form generation) and PubMedQA (biomedical QA).
  • The authors note two limitations:
    • Exact surface-form matching can miss aliases (e.g., "NYC" vs. "New York City"), which causes extra retrievals.
    • A static corpus cannot verify facts that appeared after it was indexed.
  • Code is available at github.com/ZhishanQ/QuCo-RAG.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓New York UniversityNew York · United States201 works · 100,180 citations🎓University of Illinois ChicagoChicago · United States61 works · 20,107 citations🎓Monash UniversityMelbourne · Australia60 works · 54,866 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksInformation retrieval & search · 473 works

Cite

Cite as Dehai Min, Kailin Zhang, Tongtong Wu and Lu Cheng. 2026. QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.812

@inproceedings{min2026quco,
  title = {{QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation}},
  author = {Dehai Min, Kailin Zhang, Tongtong Wu and Lu Cheng},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.812},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.812}
}

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