পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| স্বাভাবিক ভাষা উৎপাদন× | রিট্রিভাল-অগমেন্টেড জেনারেশন (RAG)× | |
|---|---|---|
| ক্ষেত্র | টেক্সট খনন | টেক্সট খনন |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era) | 2020 |
| প্রবর্তক≠ | Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018) | Lewis, Patrick et al. (Meta AI / Facebook AI Research) |
| ধরন≠ | NLP generative task — structured data to natural language | Hybrid retrieval + generation pipeline |
| মৌলিক উৎস≠ | Gatt, A. & Krahmer, E. (2018). Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation. Journal of Artificial Intelligence Research, 61, 65-170. link ↗ | Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474. DOI ↗ |
| অপর নাম | NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG) | RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG) |
| সম্পর্কিত | 7 | 7 |
| সারসংক্ষেপ≠ | Natural Language Generation (NLG) is the branch of natural language processing that automatically produces fluent, human-readable text from structured data, knowledge graphs, or semantic representations. Formalised in the classical pipeline by Reiter and Dale (2000) and surveyed comprehensively by Gatt and Krahmer (2018), NLG powers applications ranging from automated financial reporting and weather bulletins to data storytelling and conversational agents. | Retrieval-Augmented Generation (RAG) is a natural-language-processing pipeline introduced by Lewis et al. in 2020 that strengthens a large language model (LLM) with evidence fetched at inference time from an external knowledge base. Instead of relying solely on what a model memorised during training, RAG first retrieves the most relevant passages from a document index and then hands those passages to the LLM as context, grounding the generated answer in verifiable, up-to-date information. The approach reduces hallucination and allows domain-specific or time-sensitive knowledge to be injected without retraining the model. |
| ScholarGateডেটাসেট ↗ |
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