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. 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.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.14053 [cs.CL] |
| (or arXiv:2605.14053v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14053 arXiv-issued DOI via DataCite (pending registration) |
|
| Journal reference: | Advances in Artificial Intelligence IBERAMIA 2024, LNCS 15277, pp. 412 423, Springer (2025) |
| Related DOI: | https://doi.org/10.1007/978-3-031-80366-6_34
DOI(s) linking to related resources |
Submission history
From: Ignacio Sastre [view email]
[v1]
Wed, 13 May 2026 19:20:16 UTC (2,249 KB)