Large Language Models (LLMs) can generate fluent and convincing responses, but fluency does not guarantee
factual correctness. Hallucination occurs when a model produces information that is false, unsupported, or inconsistent
with available evidence. This paper reviews why hallucinations arise andexamine Retrieval-Augmented Generation (RAG)
as a practical mitigation strategy. The discussion focuses on prediction-based generation, missing or outdated knowledge,
ambiguous queries, and the absence of automatic verification. RAG addresses these limitations by retrieving relevant
information from an external knowledge source and supplying it to the generator as contextual evidence. The paper
describes the major stages of a RAG pipeline, including document ingestion, chunking, embeddings, vector storage,
retrieval, context augmentation, and answer generation. It also examines the conditions under which RAG can fail,
including poor retrieval, incomplete knowledge bases, unreliable sources, weak ranking, and incorrect interpretation of
retrieved context. Finally, the paper discusses evaluation dimensions such as context relevance, answer faithfulness, and
answer relevance, and outlines applications of RAG in education, enterprise search, technical support, research assistance,
and document question answering. The analysis concludes that RAG should be viewed as a grounding and evidence-access
mechanism rather than a guarantee of hallucination-free generation
Shyalaja L. N., Shantinath Patil, Pruthviraj S. R. et al.· International Journal for Re...· 0 citations
Surveillance systems have experienced rapid growth which results in production of large video data streams. The monitoring process for this data becomes challenging because its volume exceeds human capacity and this situation creates potential for errors. Our research presents a hybrid intelligent surveillance system which conducts automatic video analysis through its two core operational components. The system employs two primary components to achieve its objectives. The SlowFast-based model enables users to track activities through their development across various time intervals. The system employs YOLO-based models to identify critical objects which include fire and weapons and road accidents through real-time monitoring. The system achieves improved stability through the implementation of a temporal debouncing method. The system uses multiple frame detection checks to improve detection accuracy which helps prevent false alarms. The system includes a module dedicated to video summarization which creates a summary from detected activities and visual changes. The system discards unneeded video content while retaining essential information through this process. The model uses a dataset that contains 4758 video clips which display various classification types. The system reaches 85% validation accuracy which demonstrates its ability to handle new data successfully. The system operates on devices with limited resources while providing an immediate alert system to inform users about essential incidents. The system delivers an easy-to-use and effective solution for intelligent video surveillance operations.
Abdul Haq Nalband, R. U, Shashwat Dodamani et al.· 2026 7th International Confe...· 0 citations
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