Injecting Domain Expertise in LLMs - A Guide to Fine-tuning & Prompting

Learn how to inject domain-specific knowledge into LLMs for medicine, law, finance & more. Explore two powerful frameworks: fine-tuning + prompting and prompt engineering with examples.

Injecting Domain Expertise in LLMs - A Guide to Fine-tuning & Prompting

The integration of Large Language Models (LLMs) into specialized domains like medicine, law, and finance holds immense promise, pushing the boundaries of what's possible in these fields. Imagine AI assistants capable of understanding complex medical diagnoses, crafting ironclad legal arguments, or providing insightful financial forecasts.

One of the key challenges in realizing this vision is equipping LLMs with the necessary domain-specific knowledge and reasoning abilities. While readily available, general-purpose LLMs excel at general knowledge and language tasks, they often lack the depth and nuance required for specialized fields.

In this tutorial, we'll explore tried-and-tested methods to empower LLMs with domain expertise, focusing on two powerful approaches:

  1. Fine-tuning + Prompting (Framework 1): This approach involves tailoring a foundation LLM to a specific domain through targeted fine-tuning on a carefully curated dataset of domain knowledge. We'll then use strategically designed prompts to guide the model's reasoning and output. This combination allows us to create LLMs that are both knowledgeable and adept at performing specific tasks within the chosen domain.
  2. Prompting with Examples (Framework 2): Capitalizing on the inherent capabilities of large LLMs, this approach doesn't require any model retraining. Instead, we unlock domain-specific reasoning by providing the LLM with relevant context and illustrating desired behaviors through in-prompt examples. This approach proves particularly valuable when fine-tuning is impractical or when working with models already possessing vast knowledge bases.

Fine-tuning for Domain-Specific LLM

1. Domain-Specific Knowledge Infusion:


  1. Data Acquisition:
    • Web Scraping: Utilize libraries like BeautifulSoup (Python) to scrape public medical forums (with ethical considerations and respecting terms of service).
    • Datasets: Download and process open-source medical dialogue datasets:
      • MedText (available on Hugging Face)
      • Other relevant datasets like MIMIC-III (requires ethical approval)
    • ChatGPT Augmentation: Carefully craft prompts to generate diverse medical dialogues with ChatGPT and use them for fine-tuning.
  2. Data Preprocessing:
    • Cleaning: Remove irrelevant text, HTML tags, noisy characters using regular expressions and text cleaning libraries.
    • Tokenization & Formatting: Choose a tokenizer compatible with your foundation model (e.g., BPE for LLaMA) and format the data into input-output pairs suitable for fine-tuning.
    • Deduplication: Remove duplicate entries to prevent bias in the model.

2. Instruction-Driven Reasoning (3-Step Prompt):


  1. Dynamic Prompt Generation: Populate these templates programmatically with data from specific medical cases.

Prompt Template Design: Create templates for questions relevant to your application. Here's a more concrete example for diagnosis:

"Step 1. A [patient age] year-old [gender] presents with [symptoms]. They have a history of [medical history]. Relevant lab results include [lab results]. Search related knowledge and try to explain these findings. [Knowledge]"
"Step 2. Based on the provided information, what are the most likely diagnoses? Summarize in one or two sentences. [Summary]"
"Step 3. Rank the diagnoses in order of likelihood (most likely first). [Answer]" 

3. Classification Answer Enhancement:


  1. Choose Foundation Model with Output Embeddings: Select a model that makes its internal representations (embeddings) accessible (LLaMA-2 and many others do).
  2. Classification Module Design:
    • Input: Take the output embedding from the LLM corresponding to the classification point (e.g., last token for final answer).
    • Dimensionality Reduction: Experiment with:
      • Pooling layers (max pooling, average pooling)
      • Linear layers with lower output dimensions.
      • Attention mechanisms.
    • Classifier:
      • Multi-layer Perceptron (MLP): A simple feedforward network with one or more hidden layers (as in LlamaCare).
      • Other Classifiers: Test alternative classifiers like Support Vector Machines (SVMs) or Random Forests.
  3. Joint Training:
    • Include the classification module's loss function during the fine-tuning of the LLM to optimize both for better text generation and classification performance.
    • Frameworks like PyTorch and TensorFlow make it straightforward to build and train such custom architectures.

4. Continuous Evaluation & Refinement:


  1. Automated Evaluation:
    • Implement BLEU and ROUGE score calculation using libraries like nltk (Python).
    • Write scripts to automatically evaluate model outputs on benchmark datasets.
  2. Human Evaluation Pipeline:
    • Create a structured evaluation process for medical experts:
      • Define clear assessment criteria (accuracy, clarity, completeness).
      • Develop rating scales or questionnaires.
      • Collect and analyze feedback systematically.
  3. Error Analysis & Prompt Improvement:
    • Analyze misclassifications or low-quality outputs to identify patterns.
    • Refine prompts based on these patterns by:
      • Providing more specific instructions.
      • Adding clarifying examples.
      • Adjusting the format of the prompt or expected answer to reduce ambiguity.

Important Considerations:

  • Ethical Use of AI in Healthcare/Legal etc.: Adhere to strict privacy regulations (HIPAA), ensure fairness, transparency, and address potential biases in the data or model outputs.
  • Deployment and Monitoring: Continuously monitor the performance of the deployed LLM system and implement mechanisms for feedback and updates.

Integrating a Heuristics Framework into this

Heuristics are a very powerful concept for both man and machine.

Integrating Large Language Models for Dynamic Heuristic Generation
Discover a prompt engineering framework that leverages large language models (LLMs) to generate effective heuristics dynamically, enhancing decision-making and problem-solving capabilities across various domains.

Let's integrate this heuristic generation framework into your existing structure for building domain-specific (medical in this case) LLM models.

1. Domain-Specific Knowledge Infusion:

  • Incorporate Heuristics Data: Alongside medical texts, include data sources that contain heuristics or problem-solving strategies used by medical professionals.
    • Example: Case studies with expert commentaries, medical decision-making textbooks, clinical guidelines.
  • Augment with Heuristic Examples: When using ChatGPT for data augmentation, prompt it to generate medical dialogues that showcase the use of heuristics in diagnosis or treatment planning.

2. Instruction-Driven Reasoning (3-Step Prompt):

  • Steps 2 & 3: Keep the synthesis and decision steps, but the model's reasoning should now be influenced by the suggested heuristics.

Step 1: Knowledge Retrieval + Heuristic Suggestion: Modify Step 1 to also prompt the LLM to propose potential heuristics or rules-of-thumb based on the initial context.

"Step 1: A [patient age] year-old [gender] presents with [symptoms]. ...Search related knowledge AND suggest any relevant medical heuristics for this type of case. [Knowledge & Heuristics]" 

3. Classification Answer Enhancement :

  • Heuristics as Context for ECI: Provide the heuristics generated in Step 1 of the prompt as additional context to the ECI module. This can help the classifier make more informed decisions, potentially improving accuracy.

4. Continuous Evaluation & Refinement:

  • Evaluate Heuristics: Include metrics to assess the quality of the generated heuristics themselves. This could be:
    • Human Expert Review: Have medical professionals score the relevance, accuracy, and novelty of the heuristics.
    • Task-specific Success Rate: Measure how often using the suggested heuristics leads to correct diagnoses or appropriate treatment plans in benchmark cases.
  • Iterative Prompt Refinement: Based on the heuristics' evaluation, adjust prompts to encourage the LLM to generate more effective problem-solving guidelines.

Here's a Concrete Example Focusing on Diagnosis:

Input Case:
"A 25-year-old male presents with sudden onset severe headache, stiff neck, and fever."


Step 1. A 25-year-old male presents with sudden onset severe headache, stiff neck, and fever. Search related knowledge AND suggest any relevant medical heuristics for this type of case. [Knowledge & Heuristics]
Step 2: Based on the information and heuristics, what is the most likely diagnosis? Summarize in one sentence. [Summary]
Step 3: Is this diagnosis considered a medical emergency? Answer with 'Yes' or 'No'. [Answer]

Potential Output:

Step 1. [Knowledge about meningitis, causes, symptoms] Heuristics: "In young adults with sudden onset headache, stiff neck, and fever, always rule out meningitis."
Step 2. The most likely diagnosis is meningitis, a serious infection of the brain and spinal cord.
Step 3. Yes.

Benefits of this Integrated Approach:

  • More Explainable Reasoning: Explicitly generating heuristics makes the LLM's decision-making process more transparent.
  • Potential for Novel Insights: LLMs might discover useful heuristics that human experts haven't formalized yet.
  • Efficient Knowledge Transfer: Combining data-driven knowledge with heuristics can improve learning and performance, especially in data-sparse domains.

Important Notes:

  • Verification is Crucial: LLMs are prone to hallucinations. Always have human experts validate any heuristics generated by the model, especially in high-stakes domains like healthcare.
  • Ethical Considerations: Transparency in how heuristics are used is essential to ensure responsible AI in medicine.

Integrating heuristics generation into your framework, you can enhance its ability to solve complex medical problems and provide more insightful and trustworthy results.

Prompt-Driven Reasoning Framework (No Fine-Tuning)

This is example goes through the steps in the medical domain but the same process can be used in any other field.

1. Context Priming (Activate Domain Knowledge):

Comprehensive Medical Prefaces: Start the prompt with a substantial paragraph that establishes the medical context. Think of it as a mini-lecture for the LLM.
* Example: Include information about anatomy, physiology, common diseases in the relevant area (e.g., cardiovascular system, respiratory system).

* Goal: This primes the LLM's attention towards its existing medical knowledge base, making it more likely to access relevant information.
* Keyword Seeding: Strategically place relevant medical terms within the context preface to further guide the LLM's attention.

2. Heuristic Elicitation through Examples:

* Few-Shot Prompting: Instead of directly asking the LLM to generate heuristics, provide a few examples of medical cases with heuristics applied to them.
* Example:
Case 1: A 60-year-old male presents with chest pain radiating to the left arm, shortness of breath, and sweating. Heuristic: "In middle-aged men with these symptoms, always consider acute coronary syndrome." Case 2: A 10-year-old child presents with wheezing, coughing, and difficulty breathing, especially at night. Heuristic: "Recurrent wheezing in children, especially at night, points towards asthma." Now, consider this case: [New case description] ... [Continue the prompt with the remaining steps]
* Rationale: LLMs are remarkably good at pattern recognition. By seeing examples, they can learn how to apply heuristics to new, unseen cases.

3. Case-Specific Reasoning & Output:

* Step-by-Step Guidance (Optional): You can still utilize a step-by-step approach (similar to the 3-step prompt) to make the reasoning process explicit.
* Example:
Step 1: Based on the provided cases and the new case, suggest relevant medical heuristics. Step 2: Analyze the new case in detail, applying the heuristics you identified. Step 3: ... [Provide a diagnosis, treatment plan, or answer the specific medical question]
* Direct Prompting: Alternatively, you can prompt the LLM directly for the desired output, relying on the context priming and examples to guide its reasoning.
* Example: "Given the previous examples and the new patient's information, what is the most likely diagnosis and why?"

4. Retrieval-Augmented Prompting (Optional - Advanced):

* Create a Heuristics Database: Maintain a database of medical heuristics organized by specialty, symptom, or condition.
* Prompt-Guided Retrieval: At inference time, design prompts that instruct the LLM to first query the database for relevant heuristics based on the current case.

* Incorporate Retrieved Heuristics: Include the retrieved heuristics in the main prompt as context for the LLM, similar to the few-shot prompting approach.

Key Advantages of this Framework:

  • No Need for Model Retraining: Utilizes the LLM's existing knowledge, making it very efficient.
  • Leverages In-Context Learning: Few-shot learning is a powerful capability of large LLMs.
  • Flexible and Adaptable: Can be customized for various medical tasks by adjusting the context, examples, and final prompts.
  • Supports Continuous Improvement: The heuristics database (if used) can be constantly updated with new medical knowledge and best practices.

Important Considerations:

  • Prompt Complexity: These prompts can become quite large and complex. Experimentation is key to finding the right balance.
  • Hallucination Risk: Even without fine-tuning, LLMs can still generate incorrect information. Human oversight and validation in medical applications are absolutely crucial.
  • Scalability and Cost: Querying large heuristics databases and processing complex prompts can become computationally expensive at scale.

This framework offers a compelling alternative for tapping into the immense potential of large LLMs for medical reasoning without the need for complex fine-tuning procedures. Remember that combining this approach with human expertise and rigorous evaluation will be essential for creating safe and effective AI systems in healthcare.

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