hypogenic

name

hypogenic

description

Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.

Hypogenic

Overview

Hypogenic provides automated hypothesis generation and testing using large language models to accelerate scientific discovery. The framework supports three approaches: HypoGeniC (data-driven hypothesis generation), HypoRefine (synergistic literature and data integration), and Union methods (mechanistic combination of literature and data-driven hypotheses).

Quick Start

Get started with Hypogenic in minutes:

# Install the package
uv pip install hypogenic

# Clone example datasets
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data

# Run basic hypothesis generation
hypogenic_generation --config ./data/your_task/config.yaml --method hypogenic --num_hypotheses 20

# Run inference on generated hypotheses
hypogenic_inference --config ./data/your_task/config.yaml --hypotheses output/hypotheses.json

Or use Python API:

from hypogenic import BaseTask

# Create task with your configuration
task = BaseTask(config_path="./data/your_task/config.yaml")

# Generate hypotheses
task.generate_hypotheses(method="hypogenic", num_hypotheses=20)

# Run inference
results = task.inference(hypothesis_bank="./output/hypotheses.json")

When to Use This Skill

Use this skill when working on:

• Generating scientific hypotheses from observational datasets
• Testing multiple competing hypotheses systematically
• Combining literature insights with empirical patterns
• Accelerating research discovery through automated hypothesis ideation
• Domains requiring hypothesis-driven analysis: deception detection, AI-generated content identification, mental health indicators, predictive modeling, or other empirical research

Key Features

Automated Hypothesis Generation

• Generate 10-20+ testable hypotheses from data in minutes
• Iterative refinement based on validation performance
• Support for both API-based (OpenAI, Anthropic) and local LLMs

Literature Integration

• Extract insights from research papers via PDF processing
• Combine theoretical foundations with empirical patterns
• Systematic literature-to-hypothesis pipeline with GROBID

Performance Optimization

• Redis caching reduces API costs for repeated experiments
• Parallel processing for large-scale hypothesis testing
• Adaptive refinement focuses on challenging examples

Flexible Configuration

• Template-based prompt engineering with variable injection
• Custom label extraction for domain-specific tasks
• Modular architecture for easy extension

Proven Results

• 8.97% improvement over few-shot baselines
• 15.75% improvement over literature-only approaches
• 80-84% hypothesis diversity (non-redundant insights)
• Human evaluators report significant decision-making improvements

Core Capabilities

1. HypoGeniC: Data-Driven Hypothesis Generation

Generate hypotheses solely from observational data through iterative refinement.

Process:

1. Initialize with a small data subset to generate candidate hypotheses
2. Iteratively refine hypotheses based on performance
3. Replace poorly-performing hypotheses with new ones from challenging examples

Best for: Exploratory research without existing literature, pattern discovery in novel datasets

2. HypoRefine: Literature and Data Integration

Synergistically combine existing literature with empirical data through an agentic framework.

Process:

1. Extract insights from relevant research papers (typically 10 papers)
2. Generate theory-grounded hypotheses from literature
3. Generate data-driven hypotheses from observational patterns
4. Refine both hypothesis banks through iterative improvement

Best for: Research with established theoretical foundations, validating or extending existing theories

3. Union Methods

Mechanistically combine literature-only hypotheses with framework outputs.

Variants:

• Literature ∪ HypoGeniC: Combines literature hypotheses with data-driven generation
• Literature ∪ HypoRefine: Combines literature hypotheses with integrated approach

Best for: Comprehensive hypothesis coverage, eliminating redundancy while maintaining diverse perspectives

Installation

Install via pip:

uv pip install hypogenic

Optional dependencies:

• Redis server (port 6832): Enables caching of LLM responses to significantly reduce API costs during iterative hypothesis generation
• s2orc-doc2json: Required for processing literature PDFs in HypoRefine workflows
• GROBID: Required for PDF preprocessing (see Literature Processing section)

Clone example datasets:

# For HypoGeniC examples
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data

# For HypoRefine/Union examples
git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data

Dataset Format

Datasets must follow HuggingFace datasets format with specific naming conventions:

Required files:

• <TASK>_train.json: Training data
• <TASK>_val.json: Validation data
• <TASK>_test.json: Test data

Required keys in JSON:

• text_features_1 through text_features_n: Lists of strings containing feature values
• label: List of strings containing ground truth labels

Example (headline click prediction):

{
  "headline_1": [
    "What Up, Comet? You Just Got *PROBED*",
    "Scientists Made a Breakthrough in Quantum Computing"
  ],
  "headline_2": [
    "Scientists Everywhere Were Holding Their Breath Today. Here's Why.",
    "New Quantum Computer Achieves Milestone"
  ],
  "label": [
    "Headline 2 has more clicks than Headline 1",
    "Headline 1 has more clicks than Headline 2"
  ]
}

Important notes:

• All lists must have the same length
• Label format must match your extract_label() function output format
• Feature keys can be customized to match your domain (e.g., review_text, post_content, etc.)

Configuration

Each task requires a config.yaml file specifying:

Required elements:

• Dataset paths (train/val/test)
• Prompt templates for:
  • Observations generation
  • Batched hypothesis generation
  • Hypothesis inference
  • Relevance checking
  • Adaptive methods (for HypoRefine)

Template capabilities:

• Dataset placeholders for dynamic variable injection (e.g., ${text_features_1}, ${num_hypotheses})
• Custom label extraction functions for domain-specific parsing
• Role-based prompt structure (system, user, assistant roles)

Configuration structure:

task_name: your_task_name

train_data_path: ./your_task_train.json
val_data_path: ./your_task_val.json
test_data_path: ./your_task_test.json

prompt_templates:
  # Extra keys for reusable prompt components
  observations: |
    Feature 1: ${text_features_1}
    Feature 2: ${text_features_2}
    Observation: ${label}
  
  # Required templates
  batched_generation:
    system: "Your system prompt here"
    user: "Your user prompt with ${num_hypotheses} placeholder"
  
  inference:
    system: "Your inference system prompt"
    user: "Your inference user prompt"
  
  # Optional templates for advanced features
  few_shot_baseline: {...}
  is_relevant: {...}
  adaptive_inference: {...}
  adaptive_selection: {...}

Refer to references/config_template.yaml for a complete example configuration.

Literature Processing (HypoRefine/Union Methods)

To use literature-based hypothesis generation, you must preprocess PDF papers:

Step 1: Setup GROBID (first time only)

bash ./modules/setup_grobid.sh

Step 2: Add PDF files Place research papers in literature/YOUR_TASK_NAME/raw/

Step 3: Process PDFs

# Start GROBID service
bash ./modules/run_grobid.sh

# Process PDFs for your task
cd examples
python pdf_preprocess.py --task_name YOUR_TASK_NAME

This converts PDFs to structured format for hypothesis extraction. Automated literature search will be supported in future releases.

CLI Usage

Hypothesis Generation

hypogenic_generation --help

Key parameters:

• Task configuration file path
• Model selection (API-based or local)
• Generation method (HypoGeniC, HypoRefine, or Union)
• Number of hypotheses to generate
• Output directory for hypothesis banks

Hypothesis Inference

hypogenic_inference --help

Key parameters:

• Task configuration file path
• Hypothesis bank file path
• Test dataset path
• Inference method (default or multi-hypothesis)
• Output file for results

Python API Usage

For programmatic control and custom workflows, use Hypogenic directly in your Python code:

Basic HypoGeniC Generation

from hypogenic import BaseTask

# Clone example datasets first
# git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data

# Load your task with custom extract_label function
task = BaseTask(
    config_path="./data/your_task/config.yaml",
    extract_label=lambda text: extract_your_label(text)
)

# Generate hypotheses
task.generate_hypotheses(
    method="hypogenic",
    num_hypotheses=20,
    output_path="./output/hypotheses.json"
)

# Run inference
results = task.inference(
    hypothesis_bank="./output/hypotheses.json",
    test_data="./data/your_task/your_task_test.json"
)

HypoRefine/Union Methods

# For literature-integrated approaches
# git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data

# Generate with HypoRefine
task.generate_hypotheses(
    method="hyporefine",
    num_hypotheses=15,
    literature_path="./literature/your_task/",
    output_path="./output/"
)
# This generates 3 hypothesis banks:
# - HypoRefine (integrated approach)
# - Literature-only hypotheses
# - Literature∪HypoRefine (union)

Multi-Hypothesis Inference

from examples.multi_hyp_inference import run_multi_hypothesis_inference

# Test multiple hypotheses simultaneously
results = run_multi_hypothesis_inference(
    config_path="./data/your_task/config.yaml",
    hypothesis_bank="./output/hypotheses.json",
    test_data="./data/your_task/your_task_test.json"
)

Custom Label Extraction

The extract_label() function is critical for parsing LLM outputs. Implement it based on your task:

def extract_label(llm_output: str) -> str:
    """Extract predicted label from LLM inference text.
    
    Default behavior: searches for 'final answer:\s+(.*)' pattern.
    Customize for your domain-specific output format.
    """
    import re
    match = re.search(r'final answer:\s+(.*)', llm_output, re.IGNORECASE)
    if match:
        return match.group(1).strip()
    return llm_output.strip()

Important: Extracted labels must match the format of label values in your dataset for correct accuracy calculation.

Workflow Examples

Example 1: Data-Driven Hypothesis Generation (HypoGeniC)

Scenario: Detecting AI-generated content without prior theoretical framework

Steps:

1. Prepare dataset with text samples and labels (human vs. AI-generated)
2. Create config.yaml with appropriate prompt templates
3. Run hypothesis generation:

   hypogenic_generation --config config.yaml --method hypogenic --num_hypotheses 20
   

4. Run inference on test set:

   hypogenic_inference --config config.yaml --hypotheses output/hypotheses.json --test_data data/test.json
   

5. Analyze results for patterns like formality, grammatical precision, and tone differences

Example 2: Literature-Informed Hypothesis Testing (HypoRefine)

Scenario: Deception detection in hotel reviews building on existing research

Steps:

1. Collect 10 relevant papers on linguistic deception cues
2. Prepare dataset with genuine and fraudulent reviews
3. Configure config.yaml with literature processing and data generation templates
4. Run HypoRefine:

   hypogenic_generation --config config.yaml --method hyporefine --papers papers/ --num_hypotheses 15
   

5. Test hypotheses examining pronoun frequency, detail specificity, and other linguistic patterns
6. Compare literature-based and data-driven hypothesis performance

Example 3: Comprehensive Hypothesis Coverage (Union Method)

Scenario: Mental stress detection maximizing hypothesis diversity

Steps:

1. Generate literature hypotheses from mental health research papers
2. Generate data-driven hypotheses from social media posts
3. Run Union method to combine and deduplicate:

   hypogenic_generation --config config.yaml --method union --literature_hypotheses lit_hyp.json
   

4. Inference captures both theoretical constructs (posting behavior changes) and data patterns (emotional language shifts)

Performance Optimization

Caching: Enable Redis caching to reduce API costs and computation time for repeated LLM calls

Parallel Processing: Leverage multiple workers for large-scale hypothesis generation and testing

Adaptive Refinement: Use challenging examples to iteratively improve hypothesis quality

Expected Outcomes

Research using hypogenic has demonstrated:

• 14.19% accuracy improvement in AI-content detection tasks
• 7.44% accuracy improvement in deception detection tasks
• 80-84% of hypothesis pairs offering distinct, non-redundant insights
• High helpfulness ratings from human evaluators across multiple research domains

Troubleshooting

Issue: Generated hypotheses are too generic Solution: Refine prompt templates in config.yaml to request more specific, testable hypotheses

Issue: Poor inference performance Solution: Ensure dataset has sufficient training examples, adjust hypothesis generation parameters, or increase number of hypotheses

Issue: Label extraction failures Solution: Implement custom extract_label() function for domain-specific output parsing

Issue: GROBID PDF processing fails Solution: Ensure GROBID service is running (bash ./modules/run_grobid.sh) and PDFs are valid research papers

Creating Custom Tasks

To add a new task or dataset to Hypogenic:

Step 1: Prepare Your Dataset

Create three JSON files following the required format:

• your_task_train.json
• your_task_val.json
• your_task_test.json

Each file must have keys for text features (text_features_1, etc.) and label.

Step 2: Create config.yaml

Define your task configuration with:

• Task name and dataset paths
• Prompt templates for observations, generation, inference
• Any extra keys for reusable prompt components
• Placeholder variables (e.g., ${text_features_1}, ${num_hypotheses})

Step 3: Implement extract_label Function

Create a custom label extraction function that parses LLM outputs for your domain:

from hypogenic import BaseTask

def extract_my_label(llm_output: str) -> str:
    """Custom label extraction for your task.
    
    Must return labels in same format as dataset 'label' field.
    """
    # Example: Extract from specific format
    if "Final prediction:" in llm_output:
        return llm_output.split("Final prediction:")[-1].strip()
    
    # Fallback to default pattern
    import re
    match = re.search(r'final answer:\s+(.*)', llm_output, re.IGNORECASE)
    return match.group(1).strip() if match else llm_output.strip()

# Use your custom task
task = BaseTask(
    config_path="./your_task/config.yaml",
    extract_label=extract_my_label
)

Step 4: (Optional) Process Literature

For HypoRefine/Union methods:

1. Create literature/your_task_name/raw/ directory
2. Add relevant research paper PDFs
3. Run GROBID preprocessing