| Project Overview | This project direction uses recent research on tabular AI, time-series modelling, natural-language analytics, and LLM-assisted data understanding. The implementation can convert raw data into predictions, insights, visual explanations, and decision-support outputs. The reference paper, 'A Survey of Large Language Models', provides the academic base for the topic. Instead of copying the paper abstract directly, this page keeps the same research intent in a safe paraphrased form: the system uses large-language-model capabilities for explanation, reasoning, summarization, and natural-language interaction. The final student implementation can include dataset preparation, model/API integration, dashboard or app interface, result explanation, and a short documentation-ready workflow. |
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| Research Paper Title | A Survey of Large Language Models |
| Research Paper / PDF Link | Open Paper / PDF |
| Year | 2023 |
| Project Area | Data Science & Analytics |
| Project Type | Data Project |
| Required Tools / Software | Python, Pandas, NumPy, Scikit-learn, Plotly, Streamlit/Power BI, SQL |
| Main Features / Working Principle | Collect or upload relevant data, preprocess it, apply an AI/ML/LLM/RAG/software workflow, and present the result through a dashboard or application interface for Data Science & Analytics. |
| Expected Output | A working B.Tech project prototype for Data Science & Analytics with input, processing, result display, and explanation/report sections. |
| Possible Add-ons | Admin panel, PDF report export, model comparison, source citations, login system, WhatsApp help button, and deployment on cloud/hosting. |
| Get Help | Get Help on WhatsApp
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This B.Tech Computer Science & Engineering project resource connects a recent research direction with a practical implementation plan, tools, expected output, and possible extensions.