Large Language Models Need Holistically Thought in Medical Conversational QA

This is the official implementation of Large Language Models Need Holistically Thought in Medical Conversational QA.

Installation

Make sure you have Python>=3.8 installed on your machine.

pip install torch==1.8.2+cu111 torchtext==0.9.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
pip install tqdm transformers sklearn pandas numpy sentencepiece openai

Set your OpenAI API key

# https://beta.openai.com/account/api-keys
export OPENAI_API_KEY=(YOUR OPENAI API KEY)

Set arguments.

model=CODEX # {"codex", "codex-001","GLM"}. "codex" is the smallest model.
dataset=multiarith # We can use other datasets. See help for the details.
api_time_interval=4.0 # Caution. The API allows users request API up to 20 times in a minutes, otherwise errors happen.

Quick Start

HoT (our proposal)

python main.py --method=verifier_cot --model=${model} --dataset=${dataset}

CoT

# MultiArith and MedDialog are currently available.
python main.py --method=zero_shot_cot --model=${model} --dataset=${dataset}

Method

How to use GLM

The specific process is in [Github](GLM-130B/inference-with-fastertransformer.md at main · THUDM/GLM-130B (github.com)).

After we successfully load GLM in the server, we input the GLM api.

# Use GLM
python main.py --method=hot --model=GLM --dataset=${dataset} --GLM_API='xxx.xxx.xxx.xxx:5000'