hypogenic 0.1.1

A package for generating and evaluating hypotheses.

Hypothesis Generation with Large Language Models

Welcome to the GitHub repository for our paper, "Hypothesis Generation with Large Language Models". This repository is dedicated to the exploration and development of novel methodologies using large language models (LLMs) to generate hypotheses, a foundational element of scientific progress. Our work, presented in detail in the accompanying paper, highlights the capability of LLMs not just to assist but to innovate in the hypothesis generation process for scientific inquiry.

Install environment

You can directly install HypoGeniC using the following commands:

conda create --name hypogenic python=3.10
conda activate hypogenic

pip install hypogenic

OR

git clone https://github.com/ChicagoHAI/hypothesis-generation.git
cd hypothesis-generation

conda create --name hypogenic python=3.10
conda activate hypogenic

pip install -r requirements.txt
pip install -e .

[Optional]: set up Redis server for caching LLM responses

To save computation or API cost, we use Redis server to cache prompt & response pairs.

Install Redis server from source using the following commands: Note: Please install in the directory of PATH_PREFIX.

wget https://download.redis.io/redis-stable.tar.gz
tar -xzvf redis-stable.tar.gz
cd redis-stable
make

Usage

The datasets used in our paper is at HypoGeniC-datasets

1. [Optional] Start Redis server

The default port used by HypoGeniC is 6832. If you want to use a different port, please specify the port number in the --port argument.

cd $PATH_PREFIX/redis-stable/src
./redis-server --port 6832

2. Hypothesis Generation

hypogenic_generation --args

3. Hypothesis Inference

hypogenic_inference --args

Use HypoGeniC in your code

git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data
python ./examples/generation.py

More examples can be found in examples/ directory.

Add a new task or dataset

1. Data preprocessing

• To use HypoGeniC, we require users to provide a dataset in the HuggingFace datasets format:
  • <TASK>_train.json: A json file containing the training data.
  • <TASK>_test.json: A json file containing the test data.
  • <TASK>_val.json: A json file containing the validation data.
  • The json file should have keys: 'text_features_1', ... 'text_features_n', 'label'. The values corresponding to each key should be a list of strings.

2. Write config.yaml

Create the config.yaml file in the same directory as the dataset. In the config.yaml file, please specify the following fields:

task_name: <TASK>

train_data_path: ./<TASK>_train.json
val_data_path: ./<TASK>_test.json
test_data_path: ./<TASK>_val.json

prompt_templates:
  # You can use keys in your dataset as placeholders in the prompt templates
  #   For example, if your dataset has a key 'text_features_1', you can use it as ${text_features_1}
  EXTRA_KEY1: <VALUES>
  EXTRA_KEY2: <VALUES>
  # ...

  # You can use EXTRA_KEYs above as placeholders in the prompt templates
  #   For example, You can use ${EXTRA_KEY1} in the prompt templates
  # Additionally, you can use the following placeholders in the prompt templates
  #   ${num_hypotheses}: Number of hypotheses to generate
  # The prompt templates are formatted as follows:
  #   [
  #     {"role": "role1", "content": "<ROLE1_PROMPT_TEMPLATE>"}, 
  #     {"role": "role2", "content": "<ROLE2_PROMPT_TEMPLATE>"},
  #     ...
  #   ]
  batched_generation:
    role1: <ROLE1_PROMPT_TEMPLATE>
    role2: <ROLE2_PROMPT_TEMPLATE>
    # ...
  few_shot_baseline:
    role1: <ROLE1_PROMPT_TEMPLATE>
    role2: <ROLE2_PROMPT_TEMPLATE>
    # ...
  inference:
    role1: <ROLE1_PROMPT_TEMPLATE>
    role2: <ROLE2_PROMPT_TEMPLATE>
    # ...
  is_relevant:
    role1: <ROLE1_PROMPT_TEMPLATE>
    role2: <ROLE2_PROMPT_TEMPLATE>
    # ...
  adaptive_inference:
    role1: <ROLE1_PROMPT_TEMPLATE>
    role2: <ROLE2_PROMPT_TEMPLATE>
    # ...
  adaptive_selection:
    role1: <ROLE1_PROMPT_TEMPLATE>
    role2: <ROLE2_PROMPT_TEMPLATE>
    # ...

Examples

./headline_binary/headline_binary_test.json

{
  "headline_1": [
    "What Up, Comet? You Just Got *PROBED*",
    "..."
  ],
  "headline_2": [
    "Scientists Everywhere Were Holding Their Breath Today. Here's Why.",
    "..."
  ],
  "label": [
    "Headline 2 has more clicks than Headline 1",
    "..."
  ]
}

./headline_binary/config.yaml

task_name: headline_binary

train_data_path: ./headline_binary_train.json
val_data_path: ./headline_binary_test.json
test_data_path: ./headline_binary_val.json

prompt_templates:
  observations: |
    Headline 1: ${headline_1}
    Headline 2: ${headline_2}
    Observation: ${label}

  # More EXTRA_KEYs

  batched_generation:
    system: |-
      ...
      Please propose ${num_hypotheses} possible hypotheses and generate them in the format of 1. [hypothesis], 2. [hypothesis], ... ${num_hypotheses}. [hypothesis].

    user: |-
      Here are the Observations:
      ${observations}

      Please generate hypotheses that can help determine which headlines have more clicks.
      Please propose ${num_hypotheses} possible hypotheses.

      Generate them in the format of 1. [hypothesis], 2. [hypothesis], ... ${num_hypotheses}. [hypothesis]. 

      Proposed hypotheses:

  # few_shot_baseline
  # inference
  # is_relevant
  # adaptive_inference
  # adaptive_selection