Jul 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 32 references
TL;DR
The proposed WILO-BiLSTM model can perform superior to the conventional approaches and its performance results in terms of METEOR, BLEU, ROUGE, and SPICE score at training data 90% is 0.28, 0.50, 0.56, and 26.93 for the SquAD dataset, respectively.
Abstract
In the field of Natural Language Processing (NLP), intelligent virtual assistance systems or information retrieval systems have earned significant attention in recent years as they assist users and solve their queries by providing accurate answers. However, numerous studies were developed for intelligent virtual assistance systems, but they face limitations like multilingual aspects, ambiguity resolution, lack of consistency in answers, complex language structures and linguistic variations. Hence, to tackle these constraints, this research proposed an intelligent virtual assistant system using Wolf Interactive Learning Optimization-based Bidirectional Long Short-Term Memory (WILO-BiLSTM) model. The integration of BiLSTM in the model enhances the understanding of complex questions and captures semantic information, resulting in improved model performance. In addition, it processes sequential data effectively and overcomes gradient problems in long sequences. Moreover, the exploitation of the WILO algorithm in the model optimizes BiLSTM hyperparameters and significantly enhances the convergence speed with reduced computational cost. Notably, the experimental outcomes reveal that the proposed WILO-BiLSTM model can perform superior to the conventional approaches and its performance results in terms of METEOR, BLEU, ROUGE, and SPICE score at training data 90% is 0.28, 0.50, 0.56, and 26.93 for the SquAD dataset, respectively.
The deployment of Large Language Models (LLMs) for low-resource languages is challenging due to the lack of linguistic resources, sparse digital content and the absence of structured knowledge bases. In this paper, we present an adaptive knowledge-augmented framework for Mizo Large Language Models by combining Retrieval-Augmented Generation (RAG) with continual learning. This methodology harnesses semantic retrieval with dense embeddings and FAISS indexing, adaptive evidence re-ranking, parameter-efficient fine-tuning, and incremental knowledge updating to enhance factual accuracy and decrease hallucinations. Experimental evaluation shows better retrieval performance, greater text creation quality, and superior human evaluation scores than typical multilingual LLMs and static RAG methods. Moreover, the continual learning technique allows for effective integration of newly accessible Mizo resources, without re-training the model from scratch. The suggested architecture offers a scalable, stable and reusable method for the development of intelligent language technologies for Mizo and other low-resource languages.
Vanlalropuia Ralte, Abhisake Sinha· International Journal For Mu...· 0 citations
This review aims to systematically sort out the technical framework of automatic question answering system, analyze its performance bottlenecks, and explore innovative solutions based on large language model and multimodal fusion.
Xuxin Peng· Proceedings of the 3rd Inter...· 2 citations
Accurate semantic analysis and translation of complex English sentences are essential for intelligent information interaction and multilingual communication in modern digital systems, including semantic communication frameworks and electromagnetic-enabled intelligent networks. To address semantic omissions and logical inconsistencies caused by long-distance dependencies and referential ambiguity, this study proposes a GAT-BiLSTM fusion model that integrates dependency syntactic analysis with graph attention mechanisms and bidirectional long short-term memory networks. A lightweight semantic graph is first constructed to capture structural dependencies, after which graph representations are adaptively fused with contextual features through a gating mechanism to obtain unified semantic embeddings. During decoding, semantic gating and multi-head attention collaboratively enhance contextual coherence and semantic alignment. Experimental results demonstrate that the proposed model achieves a BLEU score exceeding 68.7, subject and action semantic matching scores of 0.88 and 0.84, respectively, and a syntactic structure retention rate of 72% for complex sentences. The proposed framework effectively improves translation fidelity and semantic consistency while exhibiting strong robustness for structurally complex inputs. Furthermore, the semantic modeling strategy provides methodological support for multilingual information processing, semantic communication, and intelligent human– machine interaction in electromagnetic wave propagation and wireless communication environments.
J. Leng, X. Lin· Advanced Electromagnetics· 0 citations
A comprehensive review of the evolution of NLP from traditional rule-based approaches to modern transformer models including BERT and GPT demonstrates that NLP continues to transform intelligent systems and is expected to play an increasingly significant role in the development of next-generation AI technologies.
P. Kalaiselvi· International Journal of Eme...· 0 citations
This article presents a novel approach to Intelligent Tutoring Systems (ITS) by integrating Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) to enable dynamic personalization in educational contexts. The system addresses limitations in traditional ITS that rely on static, rule-based approaches by implementing a three-layered architecture combining semantic retrieval mechanisms with generative AI capabilities. Using GPT-4 as the core LLM enhanced with a custom RAG framework, the system demonstrates improvements in response accuracy (93%), inference speed (2.1 seconds per prompt), and computational efficiency compared to a standard GPT-4 baseline, a traditional rule-based ITS, and an LLM with keyword-based retrieval. The research employs both ASSISTments (fine-grained interaction data) and EdNet (large-scale longitudinal data) datasets for evaluation. Results show that the RAG-enhanced system achieves 40% better contextual relevance compared to standard LLM implementations. The framework incorporates adaptive prompting strategies, real-time knowledge base updates, and multi-level personalization algorithms to create a dynamic educational environment.
Kuyoro Afolashade, N. Uchenna, Akinwunmi Damilare· British journal of computer,...· 0 citations
It is argued that the ability of long context should not only come from increasing the context window, but also from the ability of the model to locate, integrate and reason about important information in long text.
Jun Wu· Applied and Computational En...· 0 citations