From 82901e9fbe90e970d2bfcc7d85e8d4c4d3e9bbd2 Mon Sep 17 00:00:00 2001 From: Timothy Kassis Date: Tue, 28 Oct 2025 12:53:43 -0700 Subject: [PATCH] Add hypothesis generation using HypoGeniC and HypoRefine --- .claude-plugin/marketplace.json | 3 +- README.md | 11 +- docs/scientific-packages.md | 1 + scientific-packages/hypogenic/SKILL.md | 649 ++++++++++++++++++ .../hypogenic/references/config_template.yaml | 150 ++++ 5 files changed, 808 insertions(+), 6 deletions(-) create mode 100644 scientific-packages/hypogenic/SKILL.md create mode 100644 scientific-packages/hypogenic/references/config_template.yaml diff --git a/.claude-plugin/marketplace.json b/.claude-plugin/marketplace.json index 201ad71..4816b76 100644 --- a/.claude-plugin/marketplace.json +++ b/.claude-plugin/marketplace.json @@ -7,7 +7,7 @@ }, "metadata": { "description": "Claude scientific skills from K-Dense Inc", - "version": "1.64.0" + "version": "1.65.0" }, "plugins": [ { @@ -35,6 +35,7 @@ "./scientific-packages/etetoolkit", "./scientific-packages/flowio", "./scientific-packages/gget", + "./scientific-packages/hypogenic", "./scientific-packages/histolab", "./scientific-packages/matchms", "./scientific-packages/matplotlib", diff --git a/README.md b/README.md index 5bf08aa..e96909c 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # Claude Scientific Skills [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE.md) -[![Skills](https://img.shields.io/badge/Skills-91-brightgreen.svg)](#what-s-included) +[![Skills](https://img.shields.io/badge/Skills-92-brightgreen.svg)](#what-s-included) [![Equivalent Tools](https://img.shields.io/badge/Equivalent_Tools-500-blue.svg)](#what-s-included) A comprehensive collection of ready-to-use scientific skills for Claude, curated by the K-Dense team. @@ -45,7 +45,7 @@ These skills enable Claude to work with specialized scientific libraries and dat | Category | Count | Description | |----------|-------|-------------| | πŸ“Š **Scientific Databases** | 26 | PubMed, PubChem, UniProt, ChEMBL, COSMIC, DrugBank, AlphaFold DB, bioRxiv, and more | -| πŸ”¬ **Scientific Packages** | 56 | BioPython, RDKit, PyTorch, Scanpy, scvi-tools, ESM, SimPy, pydicom, PyHealth, Data Commons, histolab, PathML, PyLabRobot, and more | +| πŸ”¬ **Scientific Packages** | 57 | BioPython, RDKit, PyTorch, Scanpy, scvi-tools, ESM, SimPy, pydicom, PyHealth, Data Commons, histolab, PathML, PyLabRobot, HypoGeniC, and more | | πŸ”Œ **Scientific Integrations** | 7 | Benchling, DNAnexus, Opentrons, LabArchives, LatchBio, OMERO, Protocols.io | | πŸ› οΈ **Scientific Helpers** | 2 | Context initialization and resource detection utilities | | πŸ“š **Documented Workflows** | 122 | Ready-to-use examples and reference materials | @@ -78,7 +78,7 @@ Then, to install a specific set of skills: 2. Select **claude-scientific-skills** 3. Choose from: - `scientific-databases` - Access to 25 scientific databases - - `scientific-packages` - 51 specialized Python packages + - `scientific-packages` - 57 specialized Python packages - `scientific-thinking` - Analysis tools and document processing - `scientific-integrations` - Lab automation and platform integrations - `scientific-context-initialization` - Ensures Claude searches for and uses existing skills @@ -279,7 +279,7 @@ results, conclusions and providing recommendations." --- ### πŸ”¬ Scientific Packages -**56 specialized Python packages** organized by domain. +**57 specialized Python packages** organized by domain. πŸ“– **[Full Package Documentation β†’](docs/scientific-packages.md)** @@ -328,10 +328,11 @@ results, conclusions and providing recommendations."
-Additional Packages (9 packages) +Additional Packages (10 packages) - BIOMNI (Multi-omics), ETE Toolkit (Phylogenetics) - histolab (Digital pathology WSI processing and tile extraction) +- HypoGeniC (Automated hypothesis generation and testing) - Paper-2-Web (Academic paper dissemination and presentation) - PathML (Computational pathology and whole-slide image analysis) - PyLabRobot (Laboratory automation for liquid handlers, plate readers, and lab equipment) diff --git a/docs/scientific-packages.md b/docs/scientific-packages.md index 34184da..648d1bb 100644 --- a/docs/scientific-packages.md +++ b/docs/scientific-packages.md @@ -76,6 +76,7 @@ ## Multi-omics & AI Agent Frameworks - **BIOMNI** - Autonomous biomedical AI agent framework from Stanford SNAP lab for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Combines LLM reasoning with code execution and ~11GB of integrated biomedical databases (Ensembl, NCBI Gene, UniProt, PDB, AlphaFold, ClinVar, OMIM, HPO, PubMed, KEGG, Reactome, GO). Supports multiple LLM providers (Claude, GPT-4, Gemini, Groq, Bedrock). Includes A1 agent class for autonomous task decomposition, BiomniEval1 benchmark framework, and MCP server integration. Use cases: CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, protein structure analysis, literature synthesis, and multi-omics integration +- **HypoGeniC** - Automated hypothesis generation and testing using large language models to accelerate scientific discovery. Provides three frameworks: HypoGeniC (data-driven hypothesis generation from observational data), HypoRefine (synergistic approach combining literature insights with empirical patterns through an agentic system), and Union methods (mechanistic combination of literature and data-driven hypotheses). Features iterative refinement that improves hypotheses by learning from challenging examples, Redis caching for API cost reduction, and customizable YAML-based prompt templates. Includes command-line tools for generation (hypogenic_generation) and testing (hypogenic_inference). Research applications have demonstrated 14.19% accuracy improvement in AI-content detection and 7.44% in deception detection. Use cases: deception detection in reviews, AI-generated content identification, mental stress detection, exploratory research without existing literature, hypothesis-driven analysis in novel domains, and systematic exploration of competing explanations ## Scientific Communication & Publishing - **Paper-2-Web** - Autonomous pipeline for transforming academic papers into multiple promotional formats using the Paper2All system. Converts LaTeX or PDF papers into: (1) Paper2Web - interactive, layout-aware academic homepages with responsive design, interactive figures, and mobile support; (2) Paper2Video - professional presentation videos with slides, narration, cursor movements, and optional talking-head generation using Hallo2; (3) Paper2Poster - print-ready conference posters with custom dimensions, professional layouts, and institution branding. Supports GPT-4/GPT-4.1 models, batch processing, QR code generation, multi-language content, and quality assessment metrics. Use cases: conference materials, video abstracts, preprint enhancement, research promotion, poster sessions, and academic website creation diff --git a/scientific-packages/hypogenic/SKILL.md b/scientific-packages/hypogenic/SKILL.md new file mode 100644 index 0000000..e3cbf7e --- /dev/null +++ b/scientific-packages/hypogenic/SKILL.md @@ -0,0 +1,649 @@ +--- +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: + +```bash +# Install the package +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:** + +```python +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 βˆͺ HypoGeniC**: Combines literature hypotheses with data-driven generation +- **Literature βˆͺ HypoRefine**: Combines literature hypotheses with integrated approach + +**Best for:** Comprehensive hypothesis coverage, eliminating redundancy while maintaining diverse perspectives + +## Installation + +Install via pip: +```bash +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:** +```bash +# 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):** +```json +{ + "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:** +```yaml +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 +bash ./modules/setup_grobid.sh +``` + +**Step 2: Add PDF files** +Place research papers in `literature/YOUR_TASK_NAME/raw/` + +**Step 3: Process PDFs** +```bash +# 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 + +```bash +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 + +```bash +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 + +```python +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 + +```python +# 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 +# - LiteratureβˆͺHypoRefine (union) +``` + +### Multi-Hypothesis Inference + +```python +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: + +```python +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: + ```bash + hypogenic_generation --config config.yaml --method hypogenic --num_hypotheses 20 + ``` +4. Run inference on test set: + ```bash + 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: + ```bash + 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: + ```bash + 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: + +```python +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 +4. Process with `pdf_preprocess.py` + +### Step 5: Generate and Test + +Run hypothesis generation and inference using CLI or Python API: + +```bash +# 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/ +β”œβ”€β”€ hypogenic/ # Core package code +β”œβ”€β”€ hypogenic_cmd/ # CLI entry points +β”œβ”€β”€ hypothesis_agent/ # HypoRefine agent framework +β”œβ”€β”€ literature/ # Literature processing utilities +β”œβ”€β”€ modules/ # GROBID and preprocessing modules +β”œβ”€β”€ examples/ # Example scripts +β”‚ β”œβ”€β”€ generation.py # Basic HypoGeniC generation +β”‚ β”œβ”€β”€ union_generation.py # HypoRefine/Union generation +β”‚ β”œβ”€β”€ inference.py # Single hypothesis inference +β”‚ β”œβ”€β”€ multi_hyp_inference.py # Multiple hypothesis inference +β”‚ └── pdf_preprocess.py # Literature PDF processing +β”œβ”€β”€ data/ # Example datasets (clone separately) +β”œβ”€β”€ 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:** +```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:** +```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:** +```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: + +```bash +# 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 diff --git a/scientific-packages/hypogenic/references/config_template.yaml b/scientific-packages/hypogenic/references/config_template.yaml new file mode 100644 index 0000000..6052904 --- /dev/null +++ b/scientific-packages/hypogenic/references/config_template.yaml @@ -0,0 +1,150 @@ +# HypoGeniC Configuration Template +# Complete example configuration for hypothesis generation and testing + +# Dataset paths +data: + train: "data/train.json" + validation: "data/val.json" + test: "data/test.json" + + # Dataset should contain: + # - text_features_1, text_features_2, ... text_features_n (lists of strings) + # - label (list of strings) + +# Model configuration +model: + name: "gpt-4" # or "gpt-3.5-turbo", "claude-3", etc. + api_key_env: "OPENAI_API_KEY" # Environment variable for API key + temperature: 0.7 + max_tokens: 2048 + +# Redis caching (optional - reduces API costs) +cache: + enabled: true + host: "localhost" + port: 6832 + +# Hypothesis generation parameters +generation: + method: "hypogenic" # Options: "hypogenic", "hyporefine", "union" + num_hypotheses: 20 + batch_size: 5 + max_iterations: 10 + + # For HypoRefine method + literature: + papers_directory: "papers/" # Directory containing PDF files + num_papers: 10 + + # For Union methods + union: + literature_hypotheses: "literature_hypotheses.json" + deduplicate: true + +# Prompt templates +prompts: + # Observations prompt - generates initial observations from data + observations: | + Analyze the following data samples and identify patterns: + + {data_samples} + + Generate 5 distinct observations about patterns that distinguish between the two classes. + Focus on specific, testable characteristics. + + # Batched generation prompt - creates hypotheses from observations + batched_generation: | + Based on these observations about the data: + + {observations} + + Generate {num_hypotheses} distinct, testable hypotheses that could explain the differences between classes. + Each hypothesis should: + 1. Be specific and measurable + 2. Focus on a single characteristic or pattern + 3. Be falsifiable through empirical testing + + Format each hypothesis as: "Hypothesis X: [clear statement]" + + # Inference prompt - tests hypotheses against data + inference: | + Hypothesis: {hypothesis} + + Data sample: + {sample_text} + + Does this sample support or contradict the hypothesis? + Respond with: SUPPORT, CONTRADICT, or NEUTRAL + + Explanation: [brief reasoning] + + # Relevance checking prompt - filters hypotheses + relevance_check: | + Hypothesis: {hypothesis} + Task: {task_description} + + Is this hypothesis relevant and testable for the given task? + Respond with: RELEVANT or NOT_RELEVANT + + Reasoning: [brief explanation] + + # Adaptive refinement prompt - for HypoRefine + adaptive_refinement: | + Current hypothesis: {hypothesis} + + This hypothesis performed poorly on these challenging examples: + {challenging_examples} + + Generate an improved hypothesis that addresses these failures while maintaining the core insight. + + Improved hypothesis: [statement] + +# Inference configuration +inference: + method: "voting" # Options: "voting", "weighted", "ensemble" + confidence_threshold: 0.7 + max_samples: 1000 # Limit for large test sets + +# Output configuration +output: + directory: "output/" + save_intermediate: true # Save hypotheses after each iteration + format: "json" # Options: "json", "csv" + verbose: true + +# Custom label extraction (optional) +# Define a custom function in your code to parse specific output formats +label_extraction: + pattern: "PREDICTION: {label}" # Regex pattern for extracting predictions + valid_labels: ["0", "1"] # Expected label values + +# Task-specific settings +task: + name: "example_task" + description: "Binary classification task for [describe your specific domain]" + features: + - name: "text_features_1" + description: "Primary text content" + - name: "text_features_2" + description: "Additional contextual information" + labels: + - name: "0" + description: "Negative class" + - name: "1" + description: "Positive class" + +# Evaluation metrics +evaluation: + metrics: + - "accuracy" + - "precision" + - "recall" + - "f1" + cross_validation: false + num_folds: 5 + +# Logging +logging: + level: "INFO" # Options: "DEBUG", "INFO", "WARNING", "ERROR" + file: "logs/hypogenic.log" + console: true