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AI Research (LLM Focused)

This module provides a comprehensive foundation in LLM-focused research, covering core architecture, tokenization, fine-tuning, RAG, evaluation, ethics, and multimodal capabilities. Learners will engage in hands-on experiments, collaborative projects, and scientific writing to produce publication-ready research grounded in real-world applications.

Day 1-15: LLM Research Foundations​

Topics Covered​

  • Evolution of LLMs:
    • Study the progression from RNNs/LSTMs to transformer models.
    • In-depth review of “Attention is All You Need” focusing on self‐attention, positional encodings, and scaling laws.
    • Discuss challenges such as memory constraints, training stability, and the trade-offs between model size and performance.
  • Research Environment Setup:
    • Configure Jupyter Notebooks, Overleaf for drafting research papers, and Git for version control.

Hands‐on Tasks​

  • Annotate and summarize seminal papers (e.g., “BERT”, “GPT-3”) in detailed notes.
  • Set up a research repository with initial experiments and notebooks.

Deliverables​

  • A summary report with an annotated bibliography of key LLM research papers.
  • A public GitHub repository containing initial research notes, annotated Jupyter Notebooks, and sample experiments.

Day 16-30: Deep Dive into LLM Architectures & Tokenization​

Topics Covered​

  • Transformer Architectures:
    • Examine the components of transformer models (multi‐head attention, feed‐forward layers, residual connections).
  • Tokenization Techniques:
    • Compare Byte Pair Encoding (BPE), WordPiece, and SentencePiece; evaluate vocabulary size, handling of out‐of‐vocabulary tokens, and impact on sequence length.
  • Challenges:
    • Trade‐offs between token granularity and computational overhead.

Hands‐on Tasks​

  • Run experiments using HuggingFace’s Tokenizers to compare different tokenization strategies.
  • Analyze the effect of tokenization choices on model performance (speed, accuracy).

Deliverables​

  • A detailed research document on transformer architectures and tokenization with code examples.
  • A public blog post with sample experiments and a GitHub repository containing the tokenization scripts.

Day 31–45: Fine‐Tuning & Prompt Engineering​

Topics Covered​

  • Fine‐Tuning Pre‐trained Models:
    • Use HuggingFace Transformers to fine‐tune models (e.g., GPT‐2, T5) on domain‐specific datasets.
  • Prompt Engineering:
    • Techniques to design effective prompts, analyze prompt sensitivity, and optimize for few‐shot learning.
  • Challenges:
    • Stability during fine‐tuning, prompt design variability.

Hands‐on Tasks​

  • Fine‐tune a pre‐trained LLM on a custom dataset.
  • Experiment with different prompt templates and measure output quality.

Deliverables​

  • A GitHub repository showcasing fine‐tuning experiments with detailed research notes.
  • A public blog tutorial explaining prompt engineering strategies with code examples and performance comparisons.

Day 46–60: Retrieval-Augmented Generation (RAG) & Vector Databases​

Topics Covered​

  • RAG Techniques:
    • Integrate external knowledge sources to augment LLM responses.
  • Vector Databases:
    • Evaluate FAISS, Pinecone, and similar systems for efficient similarity search.
  • Challenges:
    • Efficient vector indexing, real‐time retrieval integration.

Hands‐on Tasks​

  • Build a prototype that integrates FAISS with an LLM for a retrieval‐based QA system.
  • Benchmark search times and impact on response quality.

Deliverables​

  • A comprehensive research report with experimental results integrating RAG with an LLM.
  • A public GitHub repository and blog tutorial outlining the full integration process.

Day 61–75: LLM Evaluation & Benchmarking​

Topics Covered​

  • Evaluation Metrics:
    • Perplexity, BLEU, ROUGE, and custom human‐in‐the‐loop evaluations.
  • Tools:
    • OpenAI Evals, HuggingFace Evaluate, MT‐Bench.
  • Challenges:
    • Defining standard benchmarks for diverse LLM outputs.

Hands‐on Tasks​

  • Evaluate a fine‐tuned model using multiple metrics and compare with baseline performance.
  • Develop a dashboard to visualize evaluation metrics.

Deliverables​

  • A comparative analysis report complete with charts and code examples.
  • A public blog post summarizing best practices in LLM evaluation and linking to a GitHub repository with evaluation scripts.

Day 76–90: Ethics, Bias & Fairness in LLMs​

Topics Covered​

  • Bias and Fairness:
    • Analyze inherent biases in training datasets and model outputs.
  • Mitigation Strategies:
    • Techniques such as adversarial training, bias regularization, and careful dataset curation.
  • Case Studies:
    • Real‐world examples like gender bias in translation.

Hands‐on Tasks​

  • Quantify bias in a sample LLM using defined metrics.
  • Develop mitigation experiments and document changes in output fairness.

Deliverables​

  • A detailed research paper on ethical challenges and bias mitigation in LLMs.
  • A public blog post with guidelines and a code repository demonstrating bias measurement tools.

Day 91–105: Multimodal LLMs Exploration​

Topics Covered​

  • Multimodal Integration:
    • Investigate models that combine text, images, and audio (e.g., CLIP, DALL‐E).
  • Challenges:
    • Aligning heterogeneous data representations and handling increased computational loads.

Hands‐on Tasks​

  • Experiment with a simple text‐to‐image synthesis pipeline.
  • Compare unimodal versus multimodal outputs on standard benchmarks.

Deliverables​

  • A research report with case studies and sample experiments on multimodal LLMs.
  • A public blog post discussing future research directions and a GitHub repository with demo code.

Day 106–120: Data Pre-processing & Experimental Methodologies​

Topics Covered​

  • Data Curation:
    • Best practices for dataset cleaning, normalization, and augmentation.
  • Reproducible Experiments:
    • Using version control and tools like DVC to track data and experiments.
  • Challenges:
    • Ensuring consistency across experiments and managing large datasets.

Hands‐on Tasks​

  • Build a reproducible data pipeline and integrate it with Jupyter Notebooks.
  • Demonstrate experiment tracking with a tool like MLflow or DVC.

Deliverables​

  • A comprehensive research document and public blog tutorial on experimental setups.
  • A GitHub repository containing reproducible experiments and data pipeline code.

Day 121–135: Efficient Fine‐Tuning Techniques​

Topics Covered​

  • Parameter‐Efficient Methods:
    • Study LoRA, PEFT, and adapter modules to reduce fine‐tuning cost.
  • Comparative Analysis:
    • Evaluate the trade‐offs between performance and resource savings.

Hands‐on Tasks​

  • Fine‐tune a pre‐trained LLM using LoRA and adapters.
  • Compare results (accuracy, training time, memory usage) against full‐model fine‐tuning.

Deliverables​

  • A comparative research report with detailed code examples.
  • A tutorial blog post with sample implementations and a GitHub repository containing fine‐tuning scripts.

Day 136–150: Collaborative Research Projects​

Topics Covered​

  • Team‐Based Research:
    • Propose and prototype novel LLM applications or research experiments.
  • Interdisciplinary Collaboration:
    • Bridging research gaps between academia and industry.

Hands‐on Tasks​

  • Develop a research proposal with methodology, data collection strategies, and evaluation plans.
  • Organize team meetings, assign roles, and produce a preliminary experimental design.

Deliverables​

  • A detailed project proposal document with a complete methodology and initial results.
  • A public GitHub repository showcasing prototype code and collaboration guidelines.

Day 151–165: Research Paper Writing & Peer Review​

Topics Covered​

  • Scientific Writing:
    • Best practices in drafting, structuring, and revising research papers.
  • Peer Review:
    • Conduct internal reviews and simulate conference-style feedback sessions.

Hands‐on Tasks​

  • Draft a full‐length research paper including literature review, methods, and experimental results.
  • Organize peer review sessions and collect detailed feedback.

Deliverables​

  • A draft research paper with accompanying presentation slides and a detailed methodology document.
  • Internal peer review reports and a public blog post on the process.

Day 166–180: Finalization & Dissemination of Research​

Topics Covered​

  • Refinement & Revision:
    • Incorporate peer feedback, refine experiments, and polish the final paper.
  • Dissemination:
    • Prepare for conference submissions, webinars, or internal symposiums.

Hands‐on Tasks​

  • Finalize the research paper and prepare presentation materials.
  • Organize an internal symposium or public presentation of research outcomes.

Deliverables​

  • A final research paper ready for submission and conference‐ready presentation slides.
  • A public blog post summarizing the research journey, along with comprehensive project documentation and a roadmap for future work.

Tech Stack​

  • Languages & Tools:
    • Python, Jupyter Notebooks, LaTeX
  • Libraries & Frameworks:
    • HuggingFace Transformers, PyTorch, TensorFlow, scikit‐learn
  • Evaluation Tools:
    • OpenAI Evals, HuggingFace Evaluate, MT‐Bench
  • Documentation & Collaboration:
    • Markdown, Overleaf, GitHub, Draw.io
  • Data Processing:
    • pandas, NumPy, matplotlib, seaborn