Large language models for closed-library multi-document query, test generation, and evaluation Article Swipe
Christopher Randolph
,
Adam Michaleas
,
Darrell Ricke
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/frai.2025.1592013
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/frai.2025.1592013
Introduction Learning complex, detailed, and evolving knowledge is a challenge in multiple technical professions. Relevant source knowledge is contained within many large documents and information sources with frequent updates to these documents. Knowledge tests need to be generated on new material and existing tests revised, tracking knowledge base updates. Large Language Models (LLMs) provide a framework for artificial intelligence-assisted knowledge acquisition and continued learning. Retrieval-Augmented Generation (RAG) provides a framework to leverage available, trained LLMs combined with technical area-specific knowledge bases. Methods Herein, two methods are introduced (DaaDy: document as a dictionary and SQAD: structured question answer dictionary), which together enable effective implementation of LLM-RAG question-answering on large documents. Additionally, the AI for knowledge intensive tasks (AIKIT) solution is presented for working with numerous documents for training and continuing education. AIKIT is provided as a containerized open source solution that deploys on standalone, high performance, and cloud systems. AIKIT includes LLM, RAG, vector stores, relational database, and a Ruby on Rails web interface. Results Coverage of source documents by LLM-RAG generated questions decreases as the length of documents increase. Segmenting source documents improve coverage of generated questions. The AIKIT solution enabled easy use of multiple LLM models with multimodal RAG source documents; AIKIT retains LLM-RAG responses for queries against one or multiple LLM models. Discussion AIKIT provides an easy-to-use set of tools to enable users to work with complex information using LLM-RAG capabilities. AIKIT enables easy use of multiple LLM models with retention of LLM-RAG responses.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/frai.2025.1592013
- https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1592013/pdf
- OA Status
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- OpenAlex ID
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https://openalex.org/W4413002442Canonical identifier for this work in OpenAlex
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https://doi.org/10.3389/frai.2025.1592013Digital Object Identifier
- Title
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Large language models for closed-library multi-document query, test generation, and evaluationWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-08-06Full publication date if available
- Authors
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Christopher Randolph, Adam Michaleas, Darrell RickeList of authors in order
- Landing page
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https://doi.org/10.3389/frai.2025.1592013Publisher landing page
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https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1592013/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1592013/pdfDirect OA link when available
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Computer science, Knowledge base, Leverage (statistics), Set (abstract data type), Information retrieval, World Wide Web, Language model, Data science, Artificial intelligence, Programming languageTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.base | 47 |
| abstract_inverted_index.easy | 191, 235 |
| abstract_inverted_index.high | 143 |
| abstract_inverted_index.many | 20 |
| abstract_inverted_index.need | 34 |
| abstract_inverted_index.open | 136 |
| abstract_inverted_index.that | 139 |
| abstract_inverted_index.with | 26, 76, 122, 197, 227, 241 |
| abstract_inverted_index.work | 226 |
| abstract_inverted_index.(RAG) | 66 |
| abstract_inverted_index.AIKIT | 130, 148, 188, 202, 215, 233 |
| abstract_inverted_index.Large | 49 |
| abstract_inverted_index.Rails | 160 |
| abstract_inverted_index.SQAD: | 93 |
| abstract_inverted_index.cloud | 146 |
| abstract_inverted_index.large | 21, 107 |
| abstract_inverted_index.tasks | 115 |
| abstract_inverted_index.tests | 33, 43 |
| abstract_inverted_index.these | 30 |
| abstract_inverted_index.tools | 221 |
| abstract_inverted_index.users | 224 |
| abstract_inverted_index.using | 230 |
| abstract_inverted_index.which | 98 |
| abstract_inverted_index.(LLMs) | 52 |
| abstract_inverted_index.Models | 51 |
| abstract_inverted_index.answer | 96 |
| abstract_inverted_index.bases. | 80 |
| abstract_inverted_index.enable | 100, 223 |
| abstract_inverted_index.length | 175 |
| abstract_inverted_index.models | 196, 240 |
| abstract_inverted_index.source | 15, 137, 166, 180, 200 |
| abstract_inverted_index.vector | 152 |
| abstract_inverted_index.within | 19 |
| abstract_inverted_index.(AIKIT) | 116 |
| abstract_inverted_index.(DaaDy: | 87 |
| abstract_inverted_index.Herein, | 82 |
| abstract_inverted_index.LLM-RAG | 104, 169, 204, 231, 244 |
| abstract_inverted_index.Methods | 81 |
| abstract_inverted_index.Results | 163 |
| abstract_inverted_index.against | 208 |
| abstract_inverted_index.complex | 228 |
| abstract_inverted_index.deploys | 140 |
| abstract_inverted_index.enabled | 190 |
| abstract_inverted_index.enables | 234 |
| abstract_inverted_index.improve | 182 |
| abstract_inverted_index.methods | 84 |
| abstract_inverted_index.models. | 213 |
| abstract_inverted_index.provide | 53 |
| abstract_inverted_index.queries | 207 |
| abstract_inverted_index.retains | 203 |
| abstract_inverted_index.sources | 25 |
| abstract_inverted_index.stores, | 153 |
| abstract_inverted_index.trained | 73 |
| abstract_inverted_index.updates | 28 |
| abstract_inverted_index.working | 121 |
| abstract_inverted_index.Coverage | 164 |
| abstract_inverted_index.Language | 50 |
| abstract_inverted_index.Learning | 1 |
| abstract_inverted_index.Relevant | 14 |
| abstract_inverted_index.combined | 75 |
| abstract_inverted_index.complex, | 2 |
| abstract_inverted_index.coverage | 183 |
| abstract_inverted_index.document | 88 |
| abstract_inverted_index.evolving | 5 |
| abstract_inverted_index.existing | 42 |
| abstract_inverted_index.frequent | 27 |
| abstract_inverted_index.includes | 149 |
| abstract_inverted_index.leverage | 71 |
| abstract_inverted_index.material | 40 |
| abstract_inverted_index.multiple | 11, 194, 211, 238 |
| abstract_inverted_index.numerous | 123 |
| abstract_inverted_index.provided | 132 |
| abstract_inverted_index.provides | 67, 216 |
| abstract_inverted_index.question | 95 |
| abstract_inverted_index.revised, | 44 |
| abstract_inverted_index.solution | 117, 138, 189 |
| abstract_inverted_index.systems. | 147 |
| abstract_inverted_index.together | 99 |
| abstract_inverted_index.tracking | 45 |
| abstract_inverted_index.training | 126 |
| abstract_inverted_index.updates. | 48 |
| abstract_inverted_index.Knowledge | 32 |
| abstract_inverted_index.challenge | 9 |
| abstract_inverted_index.contained | 18 |
| abstract_inverted_index.continued | 62 |
| abstract_inverted_index.database, | 155 |
| abstract_inverted_index.decreases | 172 |
| abstract_inverted_index.detailed, | 3 |
| abstract_inverted_index.documents | 22, 124, 167, 177, 181 |
| abstract_inverted_index.effective | 101 |
| abstract_inverted_index.framework | 55, 69 |
| abstract_inverted_index.generated | 37, 170, 185 |
| abstract_inverted_index.increase. | 178 |
| abstract_inverted_index.intensive | 114 |
| abstract_inverted_index.knowledge | 6, 16, 46, 59, 79, 113 |
| abstract_inverted_index.learning. | 63 |
| abstract_inverted_index.presented | 119 |
| abstract_inverted_index.questions | 171 |
| abstract_inverted_index.responses | 205 |
| abstract_inverted_index.retention | 242 |
| abstract_inverted_index.technical | 12, 77 |
| abstract_inverted_index.Discussion | 214 |
| abstract_inverted_index.Generation | 65 |
| abstract_inverted_index.Segmenting | 179 |
| abstract_inverted_index.artificial | 57 |
| abstract_inverted_index.available, | 72 |
| abstract_inverted_index.continuing | 128 |
| abstract_inverted_index.dictionary | 91 |
| abstract_inverted_index.documents. | 31, 108 |
| abstract_inverted_index.documents; | 201 |
| abstract_inverted_index.education. | 129 |
| abstract_inverted_index.interface. | 162 |
| abstract_inverted_index.introduced | 86 |
| abstract_inverted_index.multimodal | 198 |
| abstract_inverted_index.questions. | 186 |
| abstract_inverted_index.relational | 154 |
| abstract_inverted_index.responses. | 245 |
| abstract_inverted_index.structured | 94 |
| abstract_inverted_index.acquisition | 60 |
| abstract_inverted_index.easy-to-use | 218 |
| abstract_inverted_index.information | 24, 229 |
| abstract_inverted_index.standalone, | 142 |
| abstract_inverted_index.Introduction | 0 |
| abstract_inverted_index.dictionary), | 97 |
| abstract_inverted_index.performance, | 144 |
| abstract_inverted_index.professions. | 13 |
| abstract_inverted_index.Additionally, | 109 |
| abstract_inverted_index.area-specific | 78 |
| abstract_inverted_index.capabilities. | 232 |
| abstract_inverted_index.containerized | 135 |
| abstract_inverted_index.implementation | 102 |
| abstract_inverted_index.question-answering | 105 |
| abstract_inverted_index.Retrieval-Augmented | 64 |
| abstract_inverted_index.intelligence-assisted | 58 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5016624503 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I4210122954 |
| citation_normalized_percentile.value | 0.24747607 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |