Fine-tuning LLM for Prediction/Classification

1. Define Your Task & Labels

1. Task type
• Classification: e.g. “Low/Medium/High” leadership score (categorical)
• Regression: e.g. a continuous 1–10 performance rating

2. Label schema
• Decide on granularity (3 classes vs. 5 vs. continuous)
• Ensure consistency: have at least 20–30 examples per class to avoid imbalance

2. Data Preparation

1. Transcript cleaning
• Remove filler tokens, normalize speaker labels (“CEO: …”, “Audience Q:”); strip timestamps if needed.

2. Segmentation
• If full transcript > model’s max‐tokens (e.g. 2k–4k), split into coherent chunks (e.g. per Q&A segment or per minute of speech).
• Decide whether to classify each chunk then aggregate vs. treat entire transcript (with sliding window + voting).

3. Input formatting
• Prompt–Completion pairs (for causal models):
• Sequence classification (for encoder–decoder or encoder‐only models): pack transcript into input_ids, append a special classification token ([CLS]), attach a linear head.

4. Dataset splitting
• Train/Val/Test: e.g. 70/15/15
• Stratify by label to keep balanced splits

3. Choose Your Fine-Tuning Approach

1. Full Fine-Tuning
• Unfreeze all weights; add a classification head (linear + softmax or linear for regression).
• ✅ Best performance if you have ≥1k labeled examples.
• ⚠️ High GPU/memory cost; risk of catastrophic forgetting.

2. Parameter-Efficient Tuning
• Adapters (e.g. Houlsby adapters): insert small bottleneck layers between transformer blocks.
• LoRA (Low-Rank Adapters): freeze original weights; learn low-rank updates.
• Prefix / Prompt-Tuning: learn a small set of “soft prompts” prepended to input.
• ✅ Drastically fewer trainable params, cheaper; you can switch tasks fast.
• ⚠️ May need more careful hyperparam tuning.

4. Training Setup

1. Framework: Hugging Face Accelerate / PyTorch Lightning / DeepSpeed

2. Hyperparameters
• Learning rate: 1e-5–5e-5 (lower for full fine-tune, slightly higher for LoRA)
• Batch size: as large as fits (use gradient accumulation if needed)
• Epochs: 3–5 for classification; monitor val loss for early stopping
• Weight decay: 0.01–0.1 to regularize

3. Loss function
• Cross-Entropy for classification
• MSE / MAE for regression

4. Optimization tricks
• Grad-accumulation to simulate larger batches
• Mixed precision (FP16) to save memory/speed up
• Warm-up steps ≈ 10% of total steps, then linear decay

5. Handling Long Transcripts

1. Chunk + Aggregate
• Fine-tune on 1–2 k token chunks, predict per chunk, then aggregate (majority vote or average score).

2. Hierarchical Models
• First encode chunks to embeddings, then feed those embeddings into a smaller classifier (e.g. Bi-LSTM or MLP).

3. Memory-Efficient Models
• Use models with extended context (Longformer, BigBird) if you want to feed the whole transcript in one go.

6. Evaluation & Metrics

1. Classification
• Accuracy, Precision/Recall/F1 per class
• Confusion matrix to spot bias (e.g. always predicting “High”)

2. Regression
• MAE, RMSE, R²
• Correlation with human raters

3. Robustness checks
• Test on unseen speakers or different conference styles
• Ablation: remove Q&A vs. opening remarks