HypoGeniC Hypothesis Generation

HypoGeniC Hypothesis Generation

Overview

HypoGeniC automates scientific hypothesis generation and testing using LLMs on tabular datasets. Given labeled data (e.g., deception detection, AI-content identification), it generates testable hypotheses, iteratively refines them against validation performance, and runs inference to classify new samples. It supports three approaches: purely data-driven (HypoGeniC), literature-integrated (HypoRefine), and mechanistic union of both.

When to Use

• Generating testable hypotheses from labeled observational datasets without prior theory
• Systematically testing multiple competing hypotheses on empirical data
• Combining insights from research papers with data-driven pattern discovery
• Accelerating hypothesis ideation in domains like deception detection, content analysis, mental health indicators
• Benchmarking LLM-based hypothesis generation methods against few-shot baselines
• For manual hypothesis formulation frameworks, use hypothesis-generation knowhow
• For general-purpose ML classification without hypothesis interpretability, use scikit-learn-machine-learning

Prerequisites

• Python packages: hypogenic
• Optional: Redis server (port 6832) for LLM response caching; GROBID for PDF literature processing
• API keys: OpenAI, Anthropic, or compatible LLM API key in environment
• Data: Labeled JSON datasets in HypoGeniC format (see Key Concepts)

pip install hypogenic

# Optional: clone example datasets
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data
git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data_lit

Quick Start

from hypogenic import BaseTask
import re

# Custom label extractor (must match dataset label format)
def extract_label(text: str) -> str:
    match = re.search(r'final answer:\s+(.*)', text, re.IGNORECASE)