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.
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)
results = task.inference(hypothesis_bank=)
# Run inference
"./output/hypotheses.json"
When to Use This Skill
Use this skill when working on:
Generating scientific hypotheses from observational datasets
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
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:
Initialize with a small data subset to generate candidate hypotheses
Iteratively refine hypotheses based on performance
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:
Extract insights from relevant research papers (typically 10 papers)
Generate theory-grounded hypotheses from literature
Generate data-driven hypotheses from observational patterns
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
Generate literature hypotheses from mental health research papers
Generate data-driven hypotheses from social media posts
Run Union method to combine and deduplicate:
hypogenic_generation --config config.yaml --method union --literature_hypotheses lit_hyp.json
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
Create a custom label extraction function that parses LLM outputs for your domain:
from hypogenic import BaseTask
defextract_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 formatif"Final prediction:"in llm_output:
return llm_output.split("Final prediction:")[-1].strip()
# Fallback to default patternimport re
match = re.search(r'final answer:\s+(.*)', llm_output, re.IGNORECASE)
returnmatch.group(1).strip() ifmatchelse 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:
Create literature/your_task_name/raw/ directory
Add relevant research paper PDFs
Run GROBID preprocessing
Process with pdf_preprocess.py
Step 5: Generate and Test
Run hypothesis generation and inference using CLI or Python API:
# CLI approach
hypogenic_generation --config your_task/config.yaml --method hypogenic --num_hypotheses 20
hypogenic_inference --config your_task/config.yaml --hypotheses output/hypotheses.json
# Or use Python API (see Python API Usage section)
Repository Structure
Understanding the repository layout:
hypothesis-generation/
├── hypogenic/ # Core package code
├── hypogenic_cmd/ # CLI entry points
├── hypothesis_agent/ # HypoRefine agent framework
├── literature/ # Literature processing utilities
├── modules/ # GROBID and preprocessing modules
├── examples/ # Example scripts
│ ├── generation.py # Basic HypoGeniC generation
│ ├── union_generation.py # HypoRefine/Union generation
│ ├── inference.py # Single hypothesis inference
│ ├── multi_hyp_inference.py # Multiple hypothesis inference
│ └── pdf_preprocess.py # Literature PDF processing
├── data/ # Example datasets (clone separately)
├── tests/ # Unit tests
└── IO_prompting/ # Prompt templates and experiments
Key directories:
hypogenic/: Main package with BaseTask and generation logic
examples/: Reference implementations for common workflows
literature/: Tools for PDF processing and literature extraction
Description: Benchmarking framework for systematic evaluation of hypothesis generation methods
BibTeX:
@misc{liu2025hypobenchsystematicprincipledbenchmarking,
title={HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation},
author={Haokun Liu and Sicong Huang and Jingyu Hu and Yangqiaoyu Zhou and Chenhao Tan},
year={2025},
eprint={2504.11524},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2504.11524},
}
Literature Meets Data (2024)
Liu, H., Zhou, Y., Li, M., Yuan, C., & Tan, C. (2024). Literature Meets Data: A Synergistic Approach to Hypothesis Generation. arXiv preprint arXiv:2410.17309.
Description: Introduces HypoRefine and demonstrates synergistic combination of literature-based and data-driven hypothesis generation
BibTeX:
@misc{liu2024literaturemeetsdatasynergistic,
title={Literature Meets Data: A Synergistic Approach to Hypothesis Generation},
author={Haokun Liu and Yangqiaoyu Zhou and Mingxuan Li and Chenfei Yuan and Chenhao Tan},
year={2024},
eprint={2410.17309},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2410.17309},
}
Hypothesis Generation with Large Language Models (2024)
Zhou, Y., Liu, H., Srivastava, T., Mei, H., & Tan, C. (2024). Hypothesis Generation with Large Language Models. In Proceedings of EMNLP Workshop of NLP for Science.
Description: Original HypoGeniC framework for data-driven hypothesis generation
BibTeX:
@inproceedings{zhou2024hypothesisgenerationlargelanguage,
title={Hypothesis Generation with Large Language Models},
author={Yangqiaoyu Zhou and Haokun Liu and Tejes Srivastava and Hongyuan Mei and Chenhao Tan},
booktitle = {Proceedings of EMNLP Workshop of NLP for Science},
year={2024},
url={https://aclanthology.org/2024.nlp4science-1.10/},
}
