mlsanityData to 14 Oct 2026

Derivation Prompting: A Logic-Based Method for Improving Retrieval-Augmented Generation

arXiv 2605.14053 ↗First posted 13 May 2026cs.CL cs.AIcited by 0

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

Abstract (arXiv)

The application of Large Language Models to Question Answering has shown great promise, but important challenges such as hallucinations and erroneous reasoning arise when using these models, particularly in knowledge-intensive, domain-specific tasks. To address these issues, we introduce Derivation Prompting, a novel prompting technique for the generation step of the Retrieval-Augmented Generation framework. Inspired by logic derivations, this method involves deriving conclusions from initial hypotheses through the systematic application of predefined rules. It constructs a derivation tree that is interpretable and adds control over the generation process. We applied this method in a specific case study, significantly reducing unacceptable answers compared to traditional RAG and long-context window methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 15 of its 17 references that are in the map. First posted on arXiv 13 May 2026.

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References · 15

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  5. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  6. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  7. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  8. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  9. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  10. 2019Passage Re-ranking with BERTcited by 645
  11. 2022Towards Reasoning in Large Language Models: A Surveycited by 596
  12. 2023Prometheus: Inducing Fine-grained Evaluation Capability in Language Modelscited by 575
3 more
  1. 2023Evaluating Open-Domain Question Answering in the Era of Large Language Modelscited by 111
  2. 2024Faithful Logical Reasoning via Symbolic Chain-of-Thoughtcited by 100
  3. 2023Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logiccited by 35

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  1. 2023IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
  2. 2025Retrieval Augmented Question Answering: When Should LLMs Admit Ignorance?
  3. 2026Interpretable Uncertainty for Adaptive Retrieval and Reasoning in Question Answering
  4. 2026Prompt-Based Abstention Fails Under Misleading Context: A Controlled Study of Small Frozen RAG Models
  5. 2023Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs
  6. 2025LAG: Logic-Augmented Generation from a Cartesian Perspective
  7. 2024Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering
  8. 2025Emulating Retrieval Augmented Generation via Prompt Engineering for Enhanced Long Context Comprehension in LLMs