mlsanityData to 14 Oct 2026

A Library of LLM Intrinsics for Retrieval-Augmented Generation

arXiv 2504.11704 ↗First posted 16 Apr 2025cs.AIcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

In the developer community for large language models (LLMs), there is not yet a clean pattern analogous to a software library, to support very large scale collaboration. Even for the commonplace use case of Retrieval-Augmented Generation (RAG), it is not currently possible to write a RAG application against a well-defined set of APIs that are agreed upon by different LLM providers. Inspired by the idea of compiler intrinsics, we propose some elements of such a concept through introducing a library of LLM Intrinsics for RAG. An LLM intrinsic is defined as a capability that can be invoked through a well-defined API that is reasonably stable and independent of how the LLM intrinsic itself is implemented. The intrinsics in our library are released as LoRA adapters on HuggingFace, and through a software interface with clear structured input/output characteristics on top of vLLM as an inference platform, accompanied in both places with documentation and code. This article describes the intended usage, training details, and evaluations for each intrinsic, as well as compositions of multiple intrinsics.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 16 of its 24 references that are in the map. First posted on arXiv 16 Apr 2025.

Builds on

16 of 24 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely23 Sep

Other references · 15

  1. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
  2. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  3. 2019DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphscited by 903
  4. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  5. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  6. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Modelscited by 283
  7. 2023FinanceBench: A New Benchmark for Financial Question Answeringcited by 270
  8. 2023LM vs LM: Detecting Factual Errors via Cross Examinationcited by 156
  9. 2024LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QAcited by 81
  10. 2025MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systemscited by 51
  11. 2024Thermometer: Towards Universal Calibration for Large Language Modelscited by 38
  12. 2024CLAPNQ: Cohesive Long-form Answers from Passages in Natural Questions for RAG systemscited by 25
3 more
  1. 2024Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitationscited by 10
  2. 2024Multi-Document Grounded Multi-Turn Synthetic Dialog Generationcited by 7
  3. 2025Judging the Judges: A Collection of LLM-Generated Relevance Judgementscited by 7

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey
  2. 2024Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference
  3. 2024Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG
  4. 2025Tuning LLMs by RAG Principles: Towards LLM-native Memory
  5. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  6. 2024RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
  7. 2024Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach
  8. 2026RAGe: A Retrieval-Augmented Generation Evaluation Framework