Querying Structured Data Through Natural Language Using Language Models

• URL: http://arxiv.org/abs/2604.03057v1
• Date: Fri, 03 Apr 2026 14:15:15 GMT
• Title: Querying Structured Data Through Natural Language Using Language Models
• Abstract summary: This paper presents an open source methodology for allowing users to query structured non textual datasets through natural language.<n>We fine tune a compact model DeepSeek R1 Distill 8B using QLoRA with 4 bit quantization making the system suitable for deployment on commodity hardware.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents an open source methodology for allowing users to query structured non textual datasets through natural language Unlike Retrieval Augmented Generation RAG which struggles with numerical and highly structured information our approach trains an LLM to generate executable queries To support this capability we introduce a principled pipeline for synthetic training data generation producing diverse question answer pairs that capture both user intent and the semantics of the underlying dataset We fine tune a compact model DeepSeek R1 Distill 8B using QLoRA with 4 bit quantization making the system suitable for deployment on commodity hardware We evaluate our approach on a dataset describing accessibility to essential services across Durangaldea Spain The fine tuned model achieves high accuracy across monolingual multilingual and unseen location scenarios demonstrating both robust generalization and reliable query generation Our results highlight that small domain specific models can achieve high precision for this task without relying on large proprietary LLMs making this methodology suitable for resource constrained environments and adaptable to broader multi dataset systems We evaluate our approach on a dataset describing accessibility to essential services across Durangaldea Spain The fine tuned model achieves high accuracy across monolingual multilingual and unseen location scenarios demonstrating both robust generalization and reliable query generation Our results highlight that small domain specific models can achieve high precision for this task without relying on large proprietary LLMs making this methodology suitable for resource constrained environments and adaptable to broader multi dataset systems.

Related papers

• RaTA-Tool: Retrieval-based Tool Selection with Multimodal Large Language Models [57.15854852525046]
  We introduce RaTA-Tool, a novel framework for open-world multimodal tool selection.<n>Our approach enables an MLLM to convert a multimodal query into a structured task description and subsequently retrieve the most appropriate tool.<n>To further improve alignment between task descriptions and tool selection, we incorporate a preference-based optimization stage.
  arXiv  Detail & Related papers  (2026-04-16T12:47:09Z)
• Learning Retrieval Models with Sparse Autoencoders [9.69714089726174]
  SPLARE is a method to train SAE-based LSR models.<n>We show that SPLARE consistently outperforms vocabulary-based LSR in multilingual and out-of-domain settings.
  arXiv  Detail & Related papers  (2026-02-27T10:50:54Z)
• TAIJI: MCP-based Multi-Modal Data Analytics on Data Lakes [25.05627023905607]
  We envision a new multi-modal data analytics system based on the Model Context Protocol (MCP)<n>First, we define a semantic operator hierarchy tailored for querying multi-modal data in data lakes.<n>Next, we introduce an MCP-based execution framework, in which each MCP server hosts specialized foundation models optimized for specific data modalities.
  arXiv  Detail & Related papers  (2025-05-16T14:03:30Z)
• New Dataset and Methods for Fine-Grained Compositional Referring Expression Comprehension via Specialist-MLLM Collaboration [49.180693704510006]
  Referring Expression (REC) is a cross-modal task that evaluates the interplay of language understanding, image comprehension, and language-to-image grounding.<n>It serves as an essential testing ground for Multimodal Large Language Models (MLLMs)
  arXiv  Detail & Related papers  (2025-02-27T13:58:44Z)
• Deriving Coding-Specific Sub-Models from LLMs using Resource-Efficient Pruning [4.762390044282733]
  Large Language Models (LLMs) have demonstrated their exceptional performance in various complex code generation tasks.<n>To mitigate such requirements, model pruning techniques are used to create more compact models with significantly fewer parameters.<n>In this work, we explore the idea of efficiently deriving coding-specific sub-models through unstructured pruning.
  arXiv  Detail & Related papers  (2025-01-09T14:00:01Z)
• LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models [89.13128402847943]
  We present LUSIFER, a novel zero-shot approach that adapts LLM-based embedding models for multilingual tasks without requiring multilingual supervision.<n>LUSIFER's architecture combines a multilingual encoder, serving as a language-universal learner, with an LLM-based embedding model optimized for embedding-specific tasks.<n>We introduce a new benchmark encompassing 5 primary embedding tasks, 123 diverse datasets, and coverage across 14 languages.
  arXiv  Detail & Related papers  (2025-01-01T15:43:07Z)
• P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs [84.24644520272835]
  We introduce P-MMEval, a large-scale benchmark covering effective fundamental and capability-specialized datasets.<n>P-MMEval delivers consistent language coverage across various datasets and provides parallel samples.<n>We conduct extensive experiments on representative multilingual model series to compare performances across models and tasks.
  arXiv  Detail & Related papers  (2024-11-14T01:29:36Z)
• UQE: A Query Engine for Unstructured Databases [71.49289088592842]
  We investigate the potential of Large Language Models to enable unstructured data analytics. We propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
  arXiv  Detail & Related papers  (2024-06-23T06:58:55Z)
• ERATTA: Extreme RAG for Table To Answers with Large Language Models [1.3318204310917532]
  Large language models (LLMs) with retrieval augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions. We propose a unique LLM-based system where multiple LLMs can be invoked to enable data authentication, user-query routing, data-retrieval and custom prompting for question-answering capabilities from Enterprise-data tables. Our proposed system and scoring metrics achieve >90% confidence scores across hundreds of user queries in the sustainability, financial health and social media domains.
  arXiv  Detail & Related papers  (2024-05-07T02:49:59Z)
• GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning [4.8838210812204235]
  In this paper, we propose GeMQuAD - a semi-supervised learning approach, applied to a dataset generated through ICL with just one example in the target language. We iteratively identify high-quality data to enhance model performance, especially for low-resource multilingual setting. Our framework outperforms the machine translation-augmented model by 0.22/1.68 F1/EM points for Hindi and 0.82/1.37 F1/EM points for Spanish on the MLQA dataset.
  arXiv  Detail & Related papers  (2024-04-14T06:55:42Z)
• Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing [110.4684789199555]
  We introduce scenario-based semantic parsing: a variant of the original task which first requires disambiguating an utterance's "scenario" This formulation enables us to isolate coarse-grained and fine-grained aspects of the task, each of which we solve with off-the-shelf neural modules. Our model is modular, differentiable, interpretable, and allows us to garner extra supervision from scenarios.
  arXiv  Detail & Related papers  (2022-02-02T08:00:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.