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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.

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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

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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'