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:

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

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

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:

_train.json: Training data _val.json: Validation data _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:

Prepare dataset with text samples and labels (human vs. AI-generated) Create config.yaml with appropriate prompt templates Run hypothesis generation: hypogenic_generation --config config.yaml --method hypogenic --num_hypotheses 20

Run inference on test set: hypogenic_inference --config config.yaml --hypotheses output/hypotheses.json --test_data data/test.json

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:

Collect 10 relevant papers on linguistic deception cues Prepare dataset with genuine and fraudulent reviews Configure config.yaml with literature processing and data generation templates Run HypoRefine: hypogenic_generation --config config.yaml --method hyporefine --papers papers/ --num_hypotheses 15

Test hypotheses examining pronoun frequency, detail specificity, and other linguistic patterns Compare literature-based and data-driven hypothesis performance Example 3: Comprehensive Hypothesis Coverage (Union Method)

Scenario: Mental stress detection maximizing hypothesis diversity

Steps:

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 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:

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 modules/: External tool integrations (GROBID, etc.) Related Publications HypoBench (2025)

Liu, H., Huang, S., Hu, J., Zhou, Y., & Tan, C. (2025). HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation. arXiv preprint arXiv:2504.11524.

Paper: https://arxiv.org/abs/2504.11524 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.

Paper: https://arxiv.org/abs/2410.17309 Code: https://github.com/ChicagoHAI/hypothesis-generation 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.

Paper: https://aclanthology.org/2024.nlp4science-1.10/ 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/}, }

Additional Resources Official Links GitHub Repository: https://github.com/ChicagoHAI/hypothesis-generation PyPI Package: https://pypi.org/project/hypogenic/ License: MIT License Issues & Support: https://github.com/ChicagoHAI/hypothesis-generation/issues Example Datasets

Clone these repositories for ready-to-use examples:

HypoGeniC examples (data-driven only)

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

HypoRefine/Union examples (literature + data)

git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data

Community & Contributions Contributors: 7+ active contributors Stars: 89+ on GitHub Topics: research-tool, interpretability, hypothesis-generation, scientific-discovery, llm-application

For contributions or questions, visit the GitHub repository and check the issues page.

Local Resources references/

config_template.yaml - Complete example configuration file with all required prompt templates and parameters. This includes:

Full YAML structure for task configuration Example prompt templates for all methods Placeholder variable documentation Role-based prompt examples scripts/

Scripts directory is available for:

Custom data preparation utilities Format conversion tools Analysis and evaluation scripts Integration with external tools assets/

Assets directory is available for:

Example datasets and templates Sample hypothesis banks Visualization outputs Documentation supplements

安装

Install the package

Clone example datasets

Run basic hypothesis generation

Run inference on generated hypotheses

Create task with your configuration

Generate hypotheses

Run inference

For HypoGeniC examples

For HypoRefine/Union examples

Start GROBID service

Process PDFs for your task

Clone example datasets first

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

Load your task with custom extract_label function

Generate hypotheses

Run inference

For literature-integrated approaches

git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data

Generate with HypoRefine

This generates 3 hypothesis banks:

- HypoRefine (integrated approach)

- Literature-only hypotheses

- Literature∪HypoRefine (union)

Test multiple hypotheses simultaneously

Use your custom task

CLI approach

Or use Python API (see Python API Usage section)

HypoGeniC examples (data-driven only)

HypoRefine/Union examples (literature + data)