Training LLM model for asking questions

🤗Datasets
1

How should I structure my data to train an LLM that dynamically adjusts question difficulty based on my responses?

I want to train my LLM model to ask me questions and respond according to my answers.
-If I answer correctly, it should confirm my response and then present a slightly harder question.
-If I answer incorrectly, it should provide a step-by-step correct answer and then ask an easier question.

Here is my expected response from the AI model:

User: hello
AI: Hello! Please tell me your difficulty level so that I can ask you a question.
User: Medium difficulty
AI: Okay, so your difficulty level is medium. Now, tell me, what is the square root of 16?
User: 4
AI: Correct! Now, tell me the cube root of 8.
User: I think it’s 3.
AI: Oops, the answer is wrong. The cube root of 8 is 2. Now, let me ask you a little easier question… and so on.

Additionally, how should I train my model to handle pre-context (based on past interactions)? Do I need to structure my data explicitly for pre-context, or will the model automatically analyze it? Should I include long conversation-type data in my training to improve contextual understanding and how?

Also, please help me in creating and learning how to develop such datasets for training my model effectively. A sample and a useful guide will help me.

1 Like
2

For now, by Hugging Chat

To structure your data for training an LLM that dynamically adjusts question difficulty based on user responses, follow this organized approach:

1. Define Difficulty Levels

  • Categorize Questions: Organize questions into easy, medium, and hard categories. Ensure each category has a diverse range of questions covering various topics.

2. Structure Training Data

  • Interactions: Include examples of interactions where the model asks a question, the user responds, and the model adjusts difficulty. Each interaction should have:
    • Question
    • User’s answer
    • Difficulty label (Easy, Medium, Hard)

3. Pre-Context Handling

  • Conversation History: Structure the model to automatically handle past interactions by including conversation history in the training data. This allows the model to remember previous dialogue without explicit structuring.

4. Dynamic Difficulty Adjustment

  • Training Methods: Use techniques like reinforcement learning or explicit rules within the model’s architecture to adjust difficulty based on user performance. The model should choose the next question’s difficulty based on user feedback.

5. Step-by-Step Explanations

  • Explanations: Include detailed step-by-step explanations for incorrect answers in the dataset. This helps the model provide helpful feedback when a user makes a mistake.

6. Contextual Understanding

  • Long Conversations: Incorporate long conversation examples in the training data to improve the model’s ability to maintain context and flow in extended dialogues.

7. Review Existing Models

  • Adaptive Systems: Study existing adaptive systems like Duolingo or online learning platforms to understand approaches used in dynamic difficulty adjustment and apply relevant strategies.

8. User-Specific Data

  • User Tracking: Decide whether to include user-specific data or rely on conversation history for tracking progress. This choice may influence the model’s architecture and training approach.

Conclusion

Begin with a structured dataset incorporating these elements and experiment with different training techniques. Adjust the model’s architecture to manage dynamic difficulty using conversation history, ensuring it can adapt without explicit instructions. This approach should enable your LLM to effectively adjust question difficulty based on user responses.

To structure your data for training an LLM that dynamically adjusts question difficulty, follow these steps:

  1. Categorize Questions by Difficulty: Organize questions into ‘easy’, ‘medium’, and ‘hard’ categories to facilitate difficulty adjustments based on user responses.

  2. Data Structure: Each interaction in your dataset should include the following elements:

    • Question: The question posed to the user.
    • Correct Answer: The correct answer to the question.
    • User Answer: The user’s response.
    • Accuracy: Whether the user’s answer was correct or incorrect.
    • Next Difficulty: The difficulty level of the next question (e.g., ‘harder’ or ‘easier’).

    Example dataset structure:

    {
    "question": "What is the square root of 16?",
    "correct_answer": "4",
    "user_answer": "4",
    "accuracy": "correct",
    "next_difficulty": "harder"
}

  3. Include Explanations for Incorrect Answers: For incorrect user responses, provide a step-by-step explanation to guide the user.

    Example:

    {
    "question": "What is the cube root of 8?",
    "correct_answer": "2",
    "user_answer": "3",
    "accuracy": "incorrect",
    "explanation": "The cube root of a number is a value that, when multiplied by itself three times, gives the original number. 2 * 2 * 2 = 8, so the cube root of 8 is 2.",
    "next_difficulty": "easier"
}

  4. Incorporate Conversation History: To enable the model to maintain context, include multi-turn interactions in your dataset. This allows the model to adjust questions based on the entire conversation history.

  5. Use Compatible Dataset Formats: Convert your dataset into a format compatible with the Hugging Face ecosystem. The TRL library supports various formats, including preference datasets with tags like ‘dpo’. Utilize conversion scripts if necessary, such as those provided in the Hugging Face documentation.

  6. Consider Pre-Context Handling: While explicit structuring of pre-context may be beneficial, the model can often infer context from conversation history included in the dataset.

  7. Review Existing Models: Explore educational chatbots or adaptive learning systems for insights or existing frameworks that can be adapted to your project.

By creating a dataset that incorporates these elements, you can train an LLM to dynamically adjust question difficulty, provide informative feedback, and maintain contextual understanding through interactions.

3

Thanks, but most LLM models are trained on data structured as user responses and AI responses, like this:

Hello
Hi. My name is Stark. What can I do for you?

I need a sample dataset demonstrating how to implement this.

1 Like
4

For example, like this.

To effectively structure your data and train an LLM that adjusts question difficulty dynamically, follow these steps:

Step 1: Define the Dataset Structure

Create a dataset where each entry represents a conversation turn. Each entry should include:

  • Question: The question asked by the AI.
  • User Response: The user’s answer.
  • Feedback: Whether the answer was correct.
  • Next Question: The subsequent question based on the user’s response.
  • Difficulty Adjustment: Indication of whether the next question is harder or easier.

Example of a dataset entry:

{
  "question": "What is the square root of 16?",
  "user_response": "4",
  "feedback": "Correct!",
  "next_question": "What is the cube root of 8?",
  "difficulty_adjustment": "harder"
}

Step 2: Prepare the Dataset

Use the Hugging Face datasets library to create a dataset from this structure. Here’s an example of how to load and process the data:

from datasets import Dataset

# Sample dataset
data = {
    "question": ["What is the square root of 16?", "What is the cube root of 8?"],
    "user_response": ["4", "3"],
    "feedback": ["Correct!", "Oops, incorrect... The cube root of 8 is 2."],
    "next_question": ["What is the cube root of 8?", "What is 2 + 2?"],
    "difficulty_adjustment": ["harder", "easier"]
}

ds = Dataset.from_dict(data)

Step 3: Tokenization and Preprocessing

Tokenize the data and ensure it fits within the model’s maximum sequence length. Use the model’s tokenizer for this purpose:

from transformers import AutoTokenizer

model_name = "gpt2"  # Replace with your chosen LLM
tokenizer = AutoTokenizer.from_pretrained(model_name)

def tokenize_data(examples):
    inputs = []
    for i in range(len(examples["question"])):
        conversation = f"Question: {examples['question'][i]}\nUser Response: {examples['user_response'][i]}\nFeedback: {examples['feedback'][i]}\nNext Question: {examples['next_question'][i]}"
        inputs.append(conversation)
    
    tokenized = tokenizer(inputs, truncation=True, padding=True, max_length=512)
    return tokenized

ds = ds.map(tokenize_data, batched=True)

Step 4: Configure and Initialize the SFTTrainer

Set up the SFTTrainer with the model, tokenizer, dataset, and training arguments:

from transformers import AutoModelForCausalLM, SFTTrainer, TrainingArguments

model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token

training_args = TrainingArguments(
    output_dir="dynamic-question-llm",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_checkpointing=True,
    gradient_accumulation_steps=4,
    warmup_ratio=0.1,
    learning_rate=3e-5,
    weight_decay=0.1,
    save_strategy="epoch",
    logging_steps=25
)

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    dataset=ds,
    dataset_text_field="input_ids",  # or "tokenized" if used
    max_seq_length=512,
    args=training_args
)

Step 5: Train the Model

Run the training process:

trainer.train()

Step 6: Save and Export the Model

After training, save the model for future use:

trainer.save_model("dynamic-question-llm")

Conclusion

By following these steps, you structure your dataset to reflect dynamic question adjustment based on user responses and train your LLM using the Hugging Face framework. This approach ensures the model learns to adapt question difficulty, enhancing user engagement and learning effectiveness.

5

There are countless ways to create a dataset, so just create it however you like.
Many people use Python to process data for training just before training.