Derivation Prompting: A Logic-Based Method for Improving Retrieval-Augmented Generation
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
Title:Derivation Prompting: A Logic-Based Method for Improving Retrieval-Augmented Generation
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
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
Merging Methods for Multilingual Knowledge Editing for Large Language Models: An Empirical Odyssey
May 15
-
VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use
May 15
-
Mistletoe: Stealthy Acceleration-Collapse Attacks on Speculative Decoding
May 15
-
Physics-R1: An Audited Olympiad Corpus and Recipe for Visual Physics Reasoning
May 15
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.