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Related Concept Videos

Retrieval01:12

Retrieval

Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
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The Availability Heuristic

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Inductive Reasoning00:59

Inductive Reasoning

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Related Experiment Video

Updated: Jul 2, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Resource-efficient retrieval-augmented question answering for the Indian Lok Sabha dataset.

S Sivakumar1, S P Meenakshi2

  • 1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, India.

Frontiers in Artificial Intelligence
|July 1, 2026
PubMed
Summary

This study introduces a resource-efficient Retrieval-Augmented Generation (RAG) framework for legislative records. The system significantly improves factual accuracy and relevance in accessing government documents, outperforming standard models.

Keywords:
DistilGPT-2Indian Lok Sabhanatural language processingquestion answering systemretrieval-augmented generation

Related Experiment Videos

Last Updated: Jul 2, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Natural Language Processing
  • Information Retrieval
  • Computational Linguistics

Background:

  • Legislative records from the Indian Lok Sabha are primarily unstructured PDFs, limiting public access.
  • Existing keyword search and general Large Language Models (LLMs) struggle with accuracy and hallucination for these documents.

Purpose of the Study:

  • To develop a domain-specific, resource-efficient Retrieval-Augmented Generation (RAG) framework for accessing legislative records.
  • To mitigate hallucination risks and improve factual accuracy in information retrieval from unstructured government documents.

Main Methods:

  • A RAG framework was implemented using DistilGPT-2 (82M parameters) and FAISS-based semantic retrieval with Sentence-BERT embeddings.
  • The pipeline included multi-stage PDF preprocessing, semantic indexing, and context-aware response generation.
  • Evaluated on 450 queries, assessing factual accuracy and relevance via human annotators.

Main Results:

  • The RAG + DistilGPT-2 framework achieved 94% factual accuracy and a 4.6/5 relevance score.
  • Outperformed zero-shot baselines (80% factual accuracy) significantly.
  • Maintained an average inference latency of 1,800 ms on standard CPU hardware.

Conclusions:

  • Domain-specific retrieval combined with a lightweight generative model effectively reduces hallucination and computational costs.
  • The framework offers a scalable, transparent solution for e-governance, not requiring GPU infrastructure.