Course Schedule

Detailed Daily Schedule and Session Topics

Warning🚧 Being prepared

This page is being finalized ahead of the course (Aug 3–7, 2026) and may be incomplete or change before your session. The syllabus and readings are ready now.

Overview

This intensive 5-day course covers LLM-based linguistic data analysis — annotation and gold-standard construction, prompt design, and evaluation (precision/recall/F1, confusion matrices) — through lectures, tutorials, and hands-on practice, culminating in a group mini-project presentation.

Day 1 · Introduction & First Experience — Aug 3 (Mon)

# Session What we’ll do
1 Introduction to LLMs & NLP Tasks What LLMs are and how they fit linguistic analysis; the NLP tasks we’ll tackle; course overview and self-introductions.
2 Colab Onboarding & Your First LLM Call Google Colab onboarding — sign in, run a cell, read an error — then your first ai.generate_text(...) call, the data types it returns, and building a prompt with an f-string.
3 Python Practice & Mini-Project Setup Segment text into sentences (with and without a model), run the model over a list with a for loop and a function, and short Python practice — then form project groups and pick a track.

📖 Reading (before Day 1) — Skim: Abdurahman et al. (2025). See Readings →

Day 2 · Annotation, Gold Standards & Metrics — Aug 4 (Tue)

# Session What we’ll do
4 Annotation Principles & Inter-Annotator Agreement Annotation principles and the NLP pipeline for applied linguistics; an introduction to gold-standard dataset construction.
5 Hands-on: Gold-Standard Annotation & Agreement In pairs, re-annotate ~20 items using a prepared scheme; import the sheet into Colab for agreement, Cohen’s κ, and an annotator confusion matrix; iterate, then compare against the published gold.
6 Evaluation Metrics Precision, recall, F1, Cohen’s κ, and the confusion matrix, with hands-on practice in Colab.

📖 Reading (before Day 2) — Read: Eguchi & Kyle (2024); optional further reading listed. See Readings →

Day 3 · Prompt Design & Iteration — Aug 5 (Wed)

# Session What we’ll do
7 Prompt Design: Zero-shot vs Few-shot Prompt-design principles and strategies for effective prompt engineering.
8 Hands-on: LLM Classification & Prompt Iteration Run LLM-based text classification in Colab through the provided notebook, and evaluate the outputs with the prepared tools.
9 Iterative Prompt Improvement & Error Analysis Iterate the prompt over 2–3 cycles with error analysis, plus a short under-the-hood walkthrough.

📖 Reading (before Day 3) — Read: Huang & Mizumoto (2025); Kim & Lu (2024). See Readings →

Day 4 · Methodology & Pipeline Assembly — Aug 6 (Thu)

# Session What we’ll do
10 Methodology: Reproducibility, LLM Limits & Ethics Reproducibility, LLM limitations (hallucination, data contamination), and ethical issues in LLM-based research.
11 Plenary Pipeline Assembly Walk the whole pipeline as a chain of inputs and outputs, naming what each notebook consumes and produces; then write your group’s PLAN.md and get it signed off.
12 Project Work: Sample & QC the Gold Set Draw a balanced sample from your track’s pool, annotate it blind in pairs, then measure agreement, adjudicate the disagreements, and split the result into dev and test.

📖 Reading (before Day 4) — Read (in full): Abdurahman et al. (2025). See Readings →

Day 5 · Project Finalization & Presentations — Aug 7 (Fri)

# Session What we’ll do
13 Project Work: Prompt Iteration & Final Evaluation Iterate your prompt (2–3 cycles), run the final evaluation, and begin the in-class two-page report.
14 Project Work: Finalize Report & Notebook Finalize the two-page report, prepare your presentation, and submit the completed notebook.
15 Final Presentations & Wrap-up Group presentations with instructor Q&A and a course wrap-up discussion.

No new reading for Day 5 — project work only.

Important Notes

  • All times are Japan Standard Time (JST)
  • Bring your laptop to all sessions
  • Complete the readings before each day