Jul 2026· 2026 5th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)· pp. 1-6· 0 citations· 19 references
Abstract
Learning Management Systems accumulate heterogeneous data comprising unstructured course documents and structured relational databases, yet most existing tools address only one data type, limiting accessibility for non-technical users. This paper proposes a dual-pipeline large language model assistant that integrates a Retrieval-Augmented Generation pipeline for document queries with a Natural Language to SQL pipeline for database interaction. The RAG pipeline deploys Phi 3.5 and nomic-embed-text entirely on local hardware to ensure institutional data privacy, while Google Gemini generates SQL from schema metadata only. Semantic chunking based on embeddingsimilarity thresholds replaces fixed character splitting, and a lightweight generate-validate-execute-correct mechanism handles SQL failures. Evaluation using the RAGAS framework shows that semantic chunking improves answer relevancy by +0.1345 over fixed splitting. Testing on the BIRD-MINIDEV benchmark yields a $\mathbf{1 0. 5 3} \boldsymbol{\%}$ relative improvement in execution accuracy with 100% valid SQL generation. The proposed system offers a practical, privacy-compliant solution suitable for institutional deployment without large-scale infrastructure.
As Large Language Models (LLMs) become foundational to next-generation Intelligent Information Systems, the bridge between natural language interfaces and structured database systems remains a critical bottleneck. While Text-to-SQL generation enables cooperative support for complex query formulation, ensuring the rel...
M. Tritto, G. Farano, Dario Di Palma et al.· Journal of Intelligence and...· 2 citations
Due to the surge in digital document creation, intelligent systems that can effectively retrieve accurate information from unstructured PDF content have become a necessity. Current keyword-based search techniques lack semantic meaning, while large language models (LLMs), when utilized individually, produce hallucinated...
Mukesh Lakshmi Sai Medikonda, Kalva Vishnu Teja, K. Greeshma et al.· International Conference Com...· 0 citations
The empirical findings indicate that factorizing ML-aware SQL generation into four distinct stages—query routing, structured intent extraction, model or function selection, and template-guided SQL synthesis—enhances semantic controllability and token efficiency when formulating predictive natural language queries over...
AssistEM, a framework for efficient LLM adaptation to EM via principled data selection, demonstrates that selective fine-tuning not only accelerates adaptation but also improves training efficiency (requiring fewer GPU hours), enabling open-source LLMs to rival–and in some cases outperform–closed-source models.
John Bosco Mugeni, Steven J. Lynden, Toshiyuki Amagasa et al.· International Journal of Dat...· 1 citation
This work proposes MARS, a scalable knowledge graph question answering (KGQA) approach that requires no model fine-tuning, and performs a structured retrieval procedure that links question entities to the KG and iteratively retrieves relevant next-hop information.
Nikit Srivastava, Daniel Vollmers, René Speck et al.· arXiv.org· 0 citations
A design-oriented framework, an evaluation protocol, and a set of open problems for governed structured-data agents are proposed for retrieval semantics, authorization, intent recognition, entity resolution, evaluation, failure modes, and latency.
S. Ahmed· 0 citations
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