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Prompt Engineering - task_1.

Prompt Evaluation Guide: Assessing Prompt and Response Quality

1. Introduction

In this guide, we will evaluate the quality of prompts and their corresponding responses using a machine learning model. The goal is to determine whether the outputs align with specified criteria, improving the model’s prompt-handling capability through fine-tuning or adjustments. We’ll use OpenAI’s GPT-based models as our foundation, showcasing how to configure, evaluate, and visualize results locally.

This guide includes a step-by-step walkthrough for local deployment, fine-tuning, and performance assessment, complete with visualization of results to understand the quality and impact of changes.

2. Required Tools

Tools and Libraries

  • Hugging Face Transformers: For model training and configuration.
  • Datasets Library: To load and preprocess prompt-response datasets.
  • PyTorch or TensorFlow: Backend for model execution.
  • Matplotlib and Seaborn: For data visualization.
  • Python 3.8+: Required for compatibility with libraries.
  • Evaluation Metrics:

Resources

  • Model: Pretrained GPT-2 or similar transformer-based model. Download from Hugging Face Model Hub.
  • Dataset: Use datasets like squad_v2 or create a custom prompt-response dataset.
  • Environment: A local Python environment or virtual environment for isolation.

3. Installation Guide

Clone the repository for local setup.

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git clone https://github.com/huggingface/transformers.git
cd transformers

Create virtual environment.
To create a virtual environment, execute the following commands in the command line:

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pip install virtualenv

Activate the virtual environment:

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venv\Scripts\activate

Create requirements.txt in the project root directory.
Add there list of Python libraries as

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transformers
datasets
torch
matplotlib 
seaborn

Install required Python libraries from requirements.txt:

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pip install -r requirements.txt

or if you are not using virtual env, execute

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pip install transformers datasets torch matplotlib seaborn

4. Configuration Guide

  1. Prepare Configuration File
  2. Create a config.json with the following parameters:
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    {
      "model_name": "gpt-2",
      "dataset_name": "custom_dataset.json",
      "max_length": 256,
      "batch_size": 16,
      "learning_rate": 5e-5,
      "num_epochs": 3,
      "evaluation_metrics": ["bleu", "rouge-l", "perplexity"]
    }
  3. Dataset Preparation
    Ensure your dataset is in JSONL format:
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    {"prompt": "What is AI?", "response": "Artificial Intelligence is..."}
    {"prompt": "Define Machine Learning", "response": "Machine Learning is..."}

5. Core for Evaluation Task

Define the evaluation process:

  1. Load Dataset: Preprocess prompts and responses.
  2. Fine-Tune Model: Train on specific tasks to enhance response relevance.
  3. Evaluate metrics, measure:
    1. BLEU,
    2. ROUGE-L,
    3. perplexity scores for outputs.

6. Guidelines for Prompt Evaluation

Key Evaluation Areas:

  1. Relevance: Does the response match the expected answer?
  2. Clarity: Is the response clear and concise?
  3. Adaptability: Does the model adjust to different prompt complexities?
  4. Consistency: Are responses uniform in quality across test cases?

Complexity Consideration:

  • Simple prompts: Direct, factual queries.
  • Complex prompts: Context-based or multi-turn questions.

7. Main Scripts

Training Script

Save as train.py:

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from transformers import GPT2LMHeadModel, GPT2Tokenizer, Trainer, TrainingArguments
from datasets import load_dataset

# Load model and tokenizer
model_name = "gpt-2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# Load and preprocess dataset
dataset = load_dataset("json", data_files="custom_dataset.json")
def tokenize(batch):
    return tokenizer(batch["prompt"], padding="max_length", truncation=True)

tokenized_data = dataset.map(tokenize, batched=True)

# Training arguments
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=5e-5,
    per_device_train_batch_size=16,
    num_train_epochs=3
)

# Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_data["train"]
)
trainer.train()

Evaluation Script

Save as evaluate.py:

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from transformers import pipeline
from datasets import load_dataset

# Load model
model_name = "./results"
evaluator = pipeline("text-generation", model=model_name)

# Evaluate prompts
prompts = ["What is AI?", "Define Machine Learning"]
responses = [evaluator(prompt, max_length=50) for prompt in prompts]

# Metrics
metric = load_metric("bleu")
metric_score = metric.compute(predictions=responses, references=["Artificial Intelligence is...", "Machine Learning is..."])
print("BLEU Score:", metric_score)

8. Visualization and Explanation of Results

Visualization Script

Save as visualize.py:

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import matplotlib.pyplot as plt
import seaborn as sns

# Example metric scores
metrics = {"BLEU": 0.85, "ROUGE-L": 0.87, "Perplexity": 15.2}

# Plot
plt.figure(figsize=(8, 5))
sns.barplot(x=list(metrics.keys()), y=list(metrics.values()))
plt.title("Evaluation Metrics")
plt.ylabel("Scores")
plt.xlabel("Metric")
plt.show()

Analysis

  1. BLEU & ROUGE-L: Higher scores indicate better text generation quality.
  2. Perplexity: Lower scores indicate improved language model comprehension.

To improve the model’s performance here is possible to focus on the following activities based on the evaluation metrics and the provided GPT-2 configuration:

1. Data Preprocessing and Augmentation

  • Data Cleaning: Ensure the training data is clean, well-structured, and consistent. Remove noisy or irrelevant content that could negatively affect performance.
  • Augment Data: Introduce more varied examples, especially for underrepresented topics. Adding more diverse sentence structures, word choices, and contexts can help improve model robustness.

2. Prompt Optimization

  • Refining Prompts: Work on crafting more precise and detailed prompts to guide the model towards generating more accurate responses.
  • Incorporate Context: Provide context-rich prompts (e.g., multi-turn conversations) or detailed instructions to ensure the model outputs relevant and coherent responses.
  • Temperature and Sampling: Adjust the do_sample setting and modify top_k or top_p parameters to control the randomness and creativity of the model’s output. A lower temperature (e.g., 0.7) can reduce randomness and produce more deterministic outputs.

3. Model Hyperparameters Adjustment

  • Increase Layers or Heads: If you’re able to fine-tune, consider experimenting with increasing the number of layers or attention heads to help the model learn more complex patterns.
  • Experiment with n_inner: Fine-tuning the n_inner parameter (which controls the size of the intermediate layer in the transformer) may yield better results for more complex tasks.

4. Fine-Tuning GPT-2

  • Fine-Tuning with Task-Specific Data: Fine-tune the model on your specific domain or task using high-quality, labeled datasets. Fine-tuning will allow the model to learn task-specific patterns.
  • Transfer Learning: Use transfer learning techniques by starting with a pre-trained GPT-2 model, and then train it on your task-specific corpus to improve the output quality.

5. Evaluation Metric-Specific Adjustments

  • BLEU: Since BLEU is currently 0.0, which indicates poor overlap with reference texts, consider focusing on improving the lexical similarity by training on text data with high-quality references.
  • ROUGE: Improve the recall and precision for ROUGE scores by providing more informative prompts that encourage the model to capture key content and key phrases.
  • METEOR: Since METEOR considers synonyms and paraphrases, increasing the model’s understanding of semantic equivalence might improve this score. Use data augmentation or adversarial training to enhance this aspect.
  • BERTScore: BERTScore evaluates embeddings, so improving model embeddings can significantly help. You can experiment with fine-tuning GPT-2 using BERT-based models (like bert-base-uncased) for better contextual word representations.

6. Regularization Techniques

  • Dropout Regularization: Experiment with adjusting attn_pdrop, embd_pdrop, and other dropout parameters to control overfitting and improve generalization.
  • Layer Normalization: Ensure that layer normalization parameters (layer_norm_epsilon) are tuned properly to stabilize learning and avoid vanishing/exploding gradients.

7. Model Size and Parameters

  • Larger Models: If feasible, switch to larger models (e.g., GPT-2 Medium, GPT-2 Large, or GPT-3) for more capacity and better performance in complex tasks.
  • Learning Rate and Optimizer Tuning: Adjust the learning rate for better convergence. Use learning rate schedulers to optimize training over time and avoid issues like vanishing gradients or poor local minima.

8. Loss Function Adjustments

  • Loss Function Tweaks: Investigate the loss function (cross-entropy in GPT-2) to ensure it’s optimized for your specific task. Sometimes, switching the loss function can help improve performance in tasks like summarization or question-answering.

9. Sampling Strategies

  • Top-k Sampling: Adjust the top_k parameter during text generation to sample from the top K most likely words. This can prevent repetitive or irrelevant generation.
  • Nucleus Sampling: Adjust the top_p value to sample words from the cumulative probability distribution of the top P words, ensuring more diversity in the outputs.

10. Model Evaluation and Iteration

  • Cross-validation: Use cross-validation to evaluate different configurations and fine-tuned models to find the optimal setup.
  • Hyperparameter Search: Perform a hyperparameter search (e.g., grid search, random search) to find the best set of hyperparameters for improving performance metrics.

Example Implementation:

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

# Load pre-trained model and tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# Refine the prompt to improve results
prompt = "Describe the importance of artificial intelligence in healthcare."

# Generate text using refined prompt
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60)

# Decode and print the output
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)