HRGA: A HYBRID REASONING AND GENERATION ARCHITECTURE FOR MODULAR, EXPLAINABLE, AND EFFICIENT LARGE LANGUAGE MODELS
DOI:
https://doi.org/10.53555/wazxg958Keywords:
Hybrid Reasoning and Generation Architecture (HRGA), Large Language Models (LLMs), Modular AI, Retrieval-Augmented Generation (RAG), Mixture of Experts (MoE), Semantic Vector RouterAbstract
The rapid evolution of large language models has yielded remarkable generative capabilities; yet, significant chal- lenges remain regarding transparency, modularity, and compu- tational efficiency during complex reasoning tasks. To address these limitations, this paper proposes the Hybrid Reasoning and Generation Architecture, a novel framework that synergizes symbolic reasoning with neural generation to enhance inter- pretability and adaptive performance. This architecture inte- grates a modular multi-expert reasoning layer with a generative transformer backbone, facilitating dynamic switching between reasoning, retrieval, and generation modules based on the imme- diate task context. The system design leverages hybrid pipelines, utilizing knowledge-based reasoning graphs, contextual memory caching, and Mixture-of-Experts routing to optimize computa- tional resource allocation. Experimental evaluations demonstrate that the proposed framework achieves superior accuracy and reduced inference latency compared to conventional monolithic transformer models while providing transparent reasoning traces. Furthermore, the architecture exhibits cross-domain adaptability, proving effective for explainable artificial intelligence applications in sectors such as education, healthcare, and finance. Ultimately, this work offers a scalable paradigm for next-generation language models, bridging the gap between fluent text generation and logical, explainable decision-making.
References
[1]A. Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017.
[2]H. Touvron et al., “Llama 2: Open foundation and fine-tuned chat models,” arXiv preprint arXiv:2307.09288, 2023.
[3]M. Abdin et al., “Phi-3 technical report: A highly capable language model locally on your phone,” arXiv preprint arXiv:2404.14219, 2024.
[4]P. Zhang, G. Zeng, T. Wang, and W. Lu, “TinyLlama: An open-source small language model,” arXiv preprint arXiv:2401.02385, 2024.
[5]P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 9459–9474, 2020.
[6]W. Fedus, B. Zoph, and N. Shazeer, “Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,” The Journal of Machine Learning Research, vol. 23, no. 1, pp. 5232–5270, 2022.
[7]N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using Siamese BERT-networks,” in Proc. Empirical Methods in Natural Language Processing (EMNLP), 2019, pp. 3982–3992.
[8]J. Wei et al., “Chain-of-thought prompting elicits reasoning in large lan- guage models,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 35, pp. 24824–24837, 2022.
[9]E. Strubell, A. Ganesh, and A. McCallum, “Energy and policy consid- erations for deep learning in NLP,” in Proc. 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 3645–3650.
[10]T. Wolf et al., “Huggingface’s transformers: State-of-the-art natural language processing,” arXiv preprint arXiv:1910.03771, 2019.
[11]G. Gerganov, “llama.cpp: Port of Facebook’s LLaMA model in C/C++,” GitHub Repository, 2023. [Online]. Available: https://github.com/ggerganov/llama.cpp
[12]E. Bernhardsson, “Annoy: Approximate nearest neighbors in C++/Python,” GitHub Repository, 2018. [Online]. Available: https://github.com/spotify/annoy
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