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 reliability of these generated queries at inference time is a central challenge. Conventional methods rely on coarse execution-based signals, which may limit their ability to capture the nuanced semantic alignment required for high-stakes database environments. In this work, we propose the use of Outcome Reward Models (ORMs) as a fine-grained, probabilistic feedback mechanism for test-time verification in Text-to-SQL tasks. We introduce GradeSQL, a framework for training task-specific ORMs that assign scalar utility scores to candidate SQL queries based on their semantic correctness and alignment with database schema. Our approach is evaluated on the BIRD and Spider benchmarks across multiple open-source LLM families. Experimental results demonstrate that ORM-based verification consistently outperforms traditional execution-based heuristics.
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 results when they are not contextually aware of document content. In this paper, we introduce a novel real-time question answering system for PDF content using a lightweight approach to Retrieval-Augmented Generation (RAG). Our system combines a HuggingFace sentence transformer model for semantic embeddings with a FAISS vector similarity search for efficient retrieval, leveraging a Groq-hosted LLaMA 3.3 70B model for context-grounded answer generation. The system is built using LangChain for orchestration and Streamlit for web-based deployment, and requires no GPU, no fine-tuning, and no pre-indexed corpus. Evaluation across five complementary metrics—Exact Match, Token-level F1, BLEU, ROUGE, and semantic similarity—demonstrates strong alignment between generated and reference answers, with a peak semantic similarity of 0.8709 and a mean Token F1 of 0.4701, confirming the viability of this approach for real-world document intelligence.
Mukesh Lakshmi Sai Medikonda, Kalva Vishnu Teja, K. Greeshma et al.· International Conference Com...· 0 citations
Modern enterprise systems are increasingly adopting natural language (NL) interfaces for querying structured data. However, most natural language to SQL (NL2SQL) approaches focus on retrieval and aggregation over stored records, whereas many business queries require predictive computation through embedded machine learning (ML) functions. We introduce NL2SQL+, an extended NL2SQL framework for generating SQL with on-demand embedded ML functions. Given an NL query and database metadata, NL2SQL+ determines whether predictive ML is required, extracts a structured representation of the user intent, retrieves or selects a suitable ML model or function, and instantiates platform-specific training or inference SQL templates for database-native ML platforms. The framework consists of a Router for detecting ML-aware NL queries, an Extractor for intent parsing, a Model Explorer that uses an ML Model Pool for model/function selection, and a template-based SQL Generator targeting BigQuery ML and Postgres ML. We evaluate NL2SQL+ using platform-agnostic intent matching for structured intent extraction and by executing generated training SQL during ML Model Pool construction. The Router achieves an F1 score of 94%. For structured intent extraction, a fine-tuned small language model improves exact-match performance by 67.08% over OpenAI o1-mini and 9.77% over OpenAI o3-mini, and achieves nearly a fourfold gain compared with a direct LLM baseline using GPT-5-mini under the same metric. 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 database-native ML systems. These results are useful for database researchers, enterprise analytics teams, and practitioners who aim to provide natural-language access to predictive analytics over database-native ML platforms. The code, dataset, and prompts are available at https://github.com/simonjisu/NL2ML-SQL
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, S. Lynden, Toshiyuki Amagasa et al.· International Journal of Dat...· 0 citations
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.· 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.
