Prompting for hierarchical classification tasks

Prompting strategies play a vital role in improving the performance of hierarchical classification tasks, especially when leveraging large language models (LLMs) like GPT. Hierarchical classification involves organizing instances into a tree-structured or graph-structured taxonomy, where classes are dependent on each other at different levels. Designing effective prompts requires an understanding of both the taxonomy and the model’s capabilities. Below is a comprehensive guide to prompting for hierarchical classification tasks.

Understanding Hierarchical Classification

Hierarchical classification can be:

• Tree-based: Each child has only one parent, forming a strict tree.

• DAG-based (Directed Acyclic Graph): Classes can have multiple parents, allowing for a more flexible representation.

Tasks typically involve:

1. Predicting the top-level category.

2. Refining predictions through sub-categories until a leaf node (or final class) is reached.

Key Prompting Strategies

1. Top-Down Prompting (Step-by-Step Classification)
   Start by prompting the model to predict the highest-level category. Then, use the predicted class to prompt the next sub-level.

   Example:

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   Given the following input: "The patient exhibits symptoms of persistent sadness, loss of interest, and fatigue."
   Classify the condition at the top level: [Mental Health, Physical Health, Lifestyle, Other]
   

   If the model selects “Mental Health”:

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   Now classify under Mental Health: [Mood Disorders, Anxiety Disorders, Psychotic Disorders]
   

   This recursive, chain-of-thought approach mirrors human reasoning and often improves accuracy.

2. Prompt Chaining for Full Hierarchy Prediction
   Create a chained series of prompts where each level of the hierarchy is predicted in sequence.

   Example:

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   Input: "This text discusses the use of fertilizers and irrigation in farming."
   Step 1: Predict the top-level category: [Science, Agriculture, Technology, Business]
   Step 2: Under Agriculture, classify further: [Crop Production, Animal Husbandry, Agribusiness]
   Step 3: Under Crop Production: [Soil Management, Water Management, Fertilization]
   

   This reduces the complexity of the model’s prediction at each step.

3. Multi-Label Prompting (For DAG Structures)
   When nodes can belong to multiple parent categories, allow the prompt to accept multi-label answers.

   Example:

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   Classify this document into all relevant categories and subcategories:
   Document: "The new vaccine targets multiple strains of the virus and has been approved for emergency use."
   Output format: 
   - Top-level: [Health]
   - Sub-level: [Infectious Diseases, Immunization]
   

4. Few-Shot Prompting with Hierarchical Examples
   Provide examples of hierarchical labels in a structured format before asking for classification.

   Example:

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   Example 1:
   Text: "Discusses user interface and user experience design."
   Classification:
   - Top-Level: Technology
   - Sub-Level: Software Design
   - Final Class: UI/UX
   
   Example 2:
   Text: "Covers symptoms of bipolar disorder and treatments."
   Classification:
   - Top-Level: Mental Health
   - Sub-Level: Mood Disorders
   - Final Class: Bipolar Disorder
   
   Now classify:
   Text: "Explores quantum computing and its future implications."
   

   This format guides the model to emulate the hierarchical structure in its response.

5. Explicit Taxonomy Representation in the Prompt
   Provide a structured taxonomy or tree before the classification task. This primes the model with possible outputs.

   Example:

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   Taxonomy:
   - Science
     - Physics
       - Quantum Physics
     - Biology
       - Genetics
   - Technology
     - Computing
       - Quantum Computing
   
   Classify the input text into the most relevant path:
   Text: "Quantum computing is revolutionizing cryptography."
   

   Expected Output: Technology > Computing > Quantum Computing

6. Path Prediction Prompts
   Ask the model to output the entire path of classification.

   Example:

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   Classify the following into a hierarchical path:
   Input: "Machine learning algorithms for predicting stock market trends."
   
   Output format: Domain > Subdomain > Specific Area
   

   Output: Technology > Artificial Intelligence > Financial Forecasting

7. Use of System Instructions (For API Usage)
   When using models via APIs (like OpenAI), system instructions can define the classification format and logic.

   System Message:

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   You are a hierarchical classifier that maps texts to structured taxonomies. Always provide a full path and avoid ambiguous categories.
   

Best Practices

• Use consistent formatting: Clearly define the expected output (e.g., list format, full path string, etc.).

• Limit label options per prompt: Reduces ambiguity and increases accuracy.

• Iterative refinement: If unsure, prompt the model to review and verify previous classifications.

• Incorporate domain-specific terms: Domain alignment helps the model anchor its decisions.

• Use delimiters: Clearly separate instructions, inputs, and outputs (e.g., using ---, >>>, etc.).

Example Prompt for Academic Paper Classification

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Task: Classify the abstract into the following hierarchy:
- Area: [Computer Science, Biology, Physics]
- Sub-area:
   - Computer Science: [AI, Networking, Databases]
   - Biology: [Genetics, Ecology, Neuroscience]
   - Physics: [Quantum Physics, Thermodynamics, Optics]

Abstract: "This study presents a novel neural network architecture for classifying protein sequences based on structure-function relationships."

Expected Output:
- Area: Computer Science
- Sub-area: AI
Also relevant to:
- Area: Biology
- Sub-area: Genetics

Conclusion

Prompting for hierarchical classification requires structured, layered, and often multi-step inputs. Effective strategies involve breaking down the problem, priming the model with examples, and leveraging taxonomic awareness. By tailoring prompts to reflect the hierarchical nature of labels, LLMs can achieve significantly higher classification accuracy and alignment with complex taxonomies.