mlsanityData to 7 Oct 2026

Meta-prompting Optimized Retrieval-augmented Generation

arXiv 2407.03955 ↗First posted 4 Jul 2024cs.CLcited by 1

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Retrieval-augmented generation resorts to content retrieved from external sources in order to leverage the performance of large language models in downstream tasks. The excessive volume of retrieved content, the possible dispersion of its parts, or their out of focus range may happen nevertheless to eventually have a detrimental rather than an incremental effect. To mitigate this issue and improve retrieval-augmented generation, we propose a method to refine the retrieved content before it is included in the prompt by resorting to meta-prompting optimization. Put to empirical test with the demanding multi-hop question answering task from the StrategyQA dataset, the evaluation results indicate that this method outperforms a similar retrieval-augmented system but without this method by over 30%.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 29 of its 38 references that are in the map. First posted on arXiv 4 Jul 2024.

Builds on

29 of 38 references.

References · 29

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  6. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  7. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  8. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  9. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  10. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  11. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  12. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
17 more
  1. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  2. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  3. 2023Large Language Models as Optimizerscited by 830
  4. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  5. 2021Factual Probing Is [MASK]: Learning vs. Learning to Recallcited by 291
  6. 2023PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimizationcited by 226
  7. 2023EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizerscited by 189
  8. 2022GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Modelscited by 127
  9. 2019Compositional Questions Do Not Necessitate Multi-hop Reasoningcited by 87
  10. 2024PRewrite: Prompt Rewriting with Reinforcement Learningcited by 43
  11. 2019R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reasoncited by 36
  12. 2023Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*cited by 24
  13. 2024Advancing Generative AI for Portuguese with Open Decoder Gerv'asio PT*cited by 19
  14. 2024Fostering the Ecosystem of Open Neural Encoders for Portuguese with Albertina PT* Familycited by 8
  15. 2023On Meta-Promptingcited by 6
  16. 2024PORTULAN ExtraGLUE Datasets and Models: Kick-starting a Benchmark for the Neural Processing of Portuguesecited by 5
  17. 2020Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulnesscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026FLARE: Few-shot Learning-based Adaptive Reflective Enginecited by 0

Similar, not cited

Close in wording, no citation either way

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  2. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  3. 2026Evaluating Prompt Engineering Techniques for RAG in Small Language Models: A Multi-Hop QA Approach
  4. 2024Searching for Best Practices in Retrieval-Augmented Generation
  5. 2024Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering
  6. 2024R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation
  7. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  8. 2026Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem