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

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arXiv:2605. 14053v1 Announce Type: new Abstract: 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.

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Authors: Ignacio Sastre, Guillermo Moncecchi, Aiala Ros\'a
Released: 2026-05-15 (3mo ago)
Topic: Related to current project
Relation to my work: Read next because Derivation Prompting: A Logic-Based Method for Improving Retrieval-Augmented Generation overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, eval, compare, control, language, model. Source: arxiv cs.CL (NLP).
arXiv: 2605.14053

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