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Benchmark, Toolbox, and Reflection-based Method for Clinical Agent

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REFLECTOOL: Towards Reflection-Aware Tool-Augmented Clinical Agents

This is the official repository for "REFLECTOOL: Towards Reflection-Aware Tool-Augmented Clinical Agents"

[Webpage] [Paper] [Huggingface Dataset] [Leaderboard]

Outlines

🔦 About

ClinicalAgent Bench

Despite clinical agents succeeding in diverse signal interaction, they are oriented to a single clinical scenario and hence fail for broader applications. To evaluate clinical agents holistically, we propose ClinicalAgent Bench, a comprehensive medical agent benchmark consisting of 18 tasks across five key realistic clinical dimensions.

ReflecTool

Building on this, we introduce ReflectTool, a novel framework that excels at utilizing domain-specific tools within two stages. ReflectTool can search for supportive successful demonstrations from already built long-term memory to guide the tool selection strategy, and a verifier improves the tool usage according to the tool-wise experience with two verification methods--Iterative Refinement and Candidate Selection.

🏆 LeaderBoard

Methods Type Total Knowledge& Reasoning Multimodal Numerical Analysis Data Understanding Trustworthiness
MedQA MMLU BioASQ PubMedQA Avg. VQARAD SLAKE OmniMedQA Avg. MedCalc EHRSQL MIMIC-III eICU Avg. MedMentions emrQA LongHealthQA Avg. MedHalt-Rht MedVQA-Halt EHR-Halt LongHalt Avg.
MedLlama3-8b LLM 25.14 58.13 72.82 66.18 42.40 59.88 - - - - 22.45 7.92 8.65 7.40 11.61 22.93 23.10 - 23.02 9.90 - 2.23 - 6.07
Qwen2-7B LLM 38.01 54.04 69.51 71.68 48.20 60.86 - - - - 14.42 18.09 19.70 21.73 18.49 18.38 39.86 75.25 44.50 14.11 - 3.97 66.50 28.19
Llama3-8b LLM 35.62 56.32 70.34 72.82 53.80 63.32 - - - - 28.08 17.02 12.53 21.83 19.87 28.54 41.92 - 35.23 29.22 - 18.90 - 24.06
Llama3.1-8b LLM 42.46 65.20 76.58 74.92 53.00 67.43 - - - - 37.44 11.35 17.42 22.07 32.08 42.41 74.25 49.58 27.78 - 3.97 60.50 30.75
Qwen2-72B* LLM 48.76 71.25 84.48 82.85 53.00 72.90 - - - - 32.19 23.98 33.95 34.15 31.07 29.20 42.89 79.75 50.61 31.56 - 31.30 58.50 40.45
Llama3.1-70b* LLM 47.59 79.58 88.15 82.52 57.40 76.91 - - - - 48.52 16.49 25.44 26.47 29.23 25.71 31.69 80.00 45.80 28.22 - 22.48 64.50 38.40
GPT-3.5-turbo LLM 31.31 58.68 69.88 75.40 50.60 63.64 - - - - 20.53 17.57 24.31 14.30 19.18 26.88 21.64 - 24.26 9.78 - 26.55 - 18.17
MiniCPM-V-2.6 MLLM 29.28 46.58 61.16 70.23 47.20 56.29 48.78 47.12 73.70 56.53 13.28 1.61 1.63 1.88 4.60 18.92 17.42 5.25 13.86 12.44 36.89 8.91 2.25 15.12
InternVL-Chat-V1.5 MLLM 37.02 50.82 65.56 64.89 30.40 52.92 49.67 41.47 68.50 53.21 18.91 17.99 18.05 20.83 18.95 26.47 42.53 * 34.50 25.78 50.78 0.00 * 25.52
HuatuoGPT-Vision-7B MLLM 41.11 50.43 66.12 73.30 54.00 60.96 53.65 52.97 92.10 66.24 13.56 4.39 9.27 9.76 9.25 16.74 38.44 73.00 42.73 14.33 23.44 2.42 65.25 26.36
HuatuoGPT-Vision-34B MLLM 41.31 54.83 72.36 73.79 48.00 62.25 56.76 53.72 91.50 67.33 25.79 8.14 9.15 9.79 13.22 16.34 41.68 * 29.01 28.44 43.77 32.07 * 34.76
GPT-4o-mini MLLM 52.65 76.90 85.67 82.84 49.20 73.65 50.47 46.47 59.20 52.05 50.43 21.73 28.07 18.19 29.61 31.66 40.00 78.50 50.05 62.11 53.78 45.74 70.00 57.91
COT Qwen2-7b Agent 39.94 52.47 69.97 72.21 41.00 58.91 - - - - 19.10 16.06 23.81 20.95 19.98 22.83 19.92 65.75 36.17 45.56 - 31.49 57.00 44.68
ReAct Qwen2-7b Agent 42.73 51.61 67.68 80.24 48.60 62.03 35.92 39.59 72.90 49.47 18.62 18.52 25.06 34.00 24.05 22.19 24.04 41.50 29.24 49.89 35.33 61.49 48.75 48.87
CRITIC Qwen2-7b Agent 43.97 52.87 58.68 71.68 43.20 56.61 48.12 42.70 70.80 53.87 13.09 23.55 28.20 33.12 24.49 25.47 36.33 50.25 37.35 30.44 28.33 57.64 73.75 47.54
Reflexion Qwen2-7b Agent 45.25 51.78 66.48 74.60 50.80 60.92 45.68 47.97 77.20 56.95 13.37 17.56 22.16 30.23 20.83 30.30 28.92 53.00 37.41 50.55 36.33 62.91 50.75 50.14
ReflecTool (Iterative Refinement, k=2) Qwen2-7b Agent 49.37 50.12 65.47 76.37 63.20 63.79 53.88 45.71 82.90 60.83 24.68 24.20 16.92 22.08 21.97 43.69 50.27 61.00 51.65 54.44 37.99 56.73 45.25 48.60
ReflecTool (Candidates Selection, k=2) Qwen2-7b Agent 49.08 50.81 64.84 72.98 62.60 62.81 56.76 49.76 79.20 61.91 24.64 29.87 25.13 27.48 26.78 59.21 43.40 54.00 52.20 53.44 34.21 55.97 23.25 41.72
COT Qwen2-72b Agent 50.58 72.51 85.45 81.07 37.40 69.11 - - - - 29.89 18.73 23.55 25.72 24.47 24.43 52.09 81.00 52.51 57.11 - 40.79 70.75 56.22
ReAct Qwen2-72b Agent 53.31 72.43 85.67 85.38 62.40 76.47 50.09 46.01 73.00 56.37 26.46 35.97 31.45 31.87 31.44 42.11 55.02 62.75 53.29 56.89 24.75 55.78 58.50 48.98
CRITIC Qwen2-72b Agent 52.35 71.85 85.31 87.86 51.00 74.01 40.58 50.80 73.50 54.96 22.44 35.48 32.47 33.28 30.92 33.60 56.36 75.50 55.15 51.77 24.22 52.03 58.75 46.69
Reflexion Qwen2-72b Agent 56.37 70.78 84.30 87.06 65.00 76.79 54.99 50.05 77.80 60.95 22.45 40.14 31.94 33.42 31.99 52.37 58.73 64.00 58.37 59.00 33.00 59.00 64.00 53.75
ReflecTool (Iterative Refinement, k=2) Qwen2-72b Agent 59.43 73.30 84.11 84.63 65.20 76.81 57.21 48.82 85.20 63.74 36.01 47.43 33.96 36.39 38.45 54.57 67.96 68.00 63.51 59.55 38.21 57.82 63.00 54.65
ReflecTool (Candidates Selection, k=2) Qwen2-72b Agent 59.66 71.37 84.00 83.50 66.20 76.27 57.66 48.54 81.90 62.70 36.77 49.89 31.20 34.38 38.06 60.51 66.61 66.50 64.54 60.77 39.63 59.78 66.75 56.73

💫 News

  • 🔥 [2024/10/24] We release the ClinicalAgent Bench, comprised 18 tasks across five capacity dimensions!
  • 🔥 [2024/10/24] We release the code implementation of the ReflecTool!

💡 Installations

Environment

conda create -n reflectool python=3.10.8
conda activate reflectool
# install torch
pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu118
# install java
conda install -c conda-forge openjdk=21.0.4

Dependency

git clone https://github.com/BlueZeros/ReflecTool.git
cd ReflecTool
pip install -e .

ToolBox

You can find details of the tools installation in here.

📔 Dataset

Download the dataset ClinicalAgentBench from here and put it into the temp folder. The ClinicalAgentBench has the structure below:

ReflecTool/
├── ClinicalAgentBench/
│   ├── ablations/ # subset used for analysis
│   ├── train/ # dataset used for optimization stage
│   ├── test/ # dataset used for evaluation
│   ├── memory/ # few-shot samples, long-term memory and tool experience

🔬 Support Models

The support model list can be found in model_config. You should add corresponding folder path before using the model. The type of the supported models are shown below:

  • Local Models: Huggingface models load with AutoModelForCausalLM. We also support the vllm accelerate for these models (automatically used if vllm is installed).
    • Llama3
    • Llama3.1
    • Qwen1.5
    • Qwen2
    • Qwen2.5
  • OpenAI Models: OpenAI Model list.
    • GPT-3.5-turbo
    • GPT-4o-mini
    • GPT-4o
    • Note: need to set environment variables OPENAI_API_KEY={your_openai_api_key}.
  • MultiModal LLM: HuatuoGPT-Vision and MiniCPM.
    • HuatuoGPT-Vision
    • MiniCPM-V-2.6
    • InternVL-Chat-V1.5

📝 Evaluation

Baselines

Models 
DATA_PATH=./ClinicalAgentBench
TASK_PATH=test

OUTPUT_PATH=./results/${TASK_PATH}
MEMORY_PATH=${DATA_PATH}/memory

DATASET=medqa
MODEL=qewn2-7b

EXP_NAME=${MODEL}
mkdir -p ${OUTPUT_PATH}/${DATASET}/${EXP_NAME}

python run.py \
    --data-path ${DATA_PATH} \
    --output-path ${OUTPUT_PATH} \
    --task-name ${TASK_PATH} \
    --test-split ${DATASET} \
    --exp-name ${EXP_NAME} \
    --model ${MODEL} \
    --log-print \
    --prompt-debug \
    --resume
Reflexion Agent 
DATA_PATH=./ClinicalAgentBench
TASK_PATH=test

OUTPUT_PATH=./results/${TASK_PATH}
MEMORY_PATH=${DATA_PATH}/memory

DATASET="medqa"
MODEL=qwen2-7b
ACTIONS="all_wo_mm"
FEWSHOT=1

EXP_NAME=reflexion_${MODEL}-few_shot_${FEWSHOT}-${ACTIONS}
mkdir -p ${OUTPUT_PATH}/${DATASET}/${EXP_NAME}

python run.py \
    --data-path ${DATA_PATH} \
    --output-path ${OUTPUT_PATH} \
    --task-name ${TASK_PATH} \
    --test-split ${DATASET} \
    --exp-name ${EXP_NAME} \
    --agent "reflexion" \
    --model ${MODEL} \
    --actions ${ACTIONS} \
    --memory-path ${MEMORY_PATH} \
    --memory-type "reflexion_standard" \
    --few-shot ${FEWSHOT} \
    --resume
CRITIC Agent 
DATA_PATH=./ClinicalAgentBench
TASK_PATH=test

OUTPUT_PATH=./results/${TASK_PATH}
MEMORY_PATH=${DATA_PATH}/memory

DATASET="medqa"
MODEL=qwen2-72b-int4

ACTIONS="all_wo_mm"
FEWSHOT=1

EXP_NAME=critic_${MODEL}-few_shot_${FEWSHOT}-${ACTIONS}
mkdir -p ${OUTPUT_PATH}/${DATASET}/${EXP_NAME}

python run.py \
    --data-path ${DATA_PATH} \
    --output-path ${OUTPUT_PATH} \
    --task-name ${TASK_PATH} \
    --test-split ${DATASET} \
    --exp-name ${EXP_NAME} \
    --agent "critic" \
    --model ${MODEL} \
    --actions ${ACTIONS} \
    --memory-path ${MEMORY_PATH} \
    --memory-type "critic_standard" \
    --few-shot ${FEWSHOT} \
    --resume \
    --log-print \
    --prompt-debug

ReflecTool

Optimization

optimization on medqa dataset (Knowledge task example) 
DATA_PATH=./ClinicalAgentBench
TASK_PATH=train

OUTPUT_PATH=./results/${TASK_PATH}
MEMORY_PATH=${DATA_PATH}/my_memory

domain=medqa
MODEL=qwen2-7b
ACTIONS="all_wo_mm" # tool type. note that `all` and `mm` actions should use 2 gpu.
FEWSHOT=0 # num of few-shot demonstration

EXP_NAME=train-${MODEL}-few_shot_${FEWSHOT}-${ACTIONS}
mkdir -p ${OUTPUT_PATH}/${domain}/${EXP_NAME}

python train.py \
    --data-path ${DATA_PATH} \
    --output-path ${OUTPUT_PATH} \
    --task-name ${TASK_PATH} \
    --test-split ${domain} \
    --exp-name ${EXP_NAME} \
    --model ${MODEL} \
    --actions ${ACTIONS} \
    --memory-path ${MEMORY_PATH} \
    --max-exec-steps 15 \
    --few-shot ${FEWSHOT} \
    --write-memory \
    --memory-type task \
    --log-print \
    --resume
optimization on slake dataset (MultiModal task example) 
DATA_PATH=./ClinicalAgentBench
TASK_PATH=train

OUTPUT_PATH=./results/${TASK_PATH}
MEMORY_PATH=${DATA_PATH}/my_memory

domain=slake
MODEL=qwen2-7b
ACTIONS="all" # tool type. note that `all` and `mm` actions should use 2 gpu.
FEWSHOT=0 # num of few-shot demonstration

EXP_NAME=train-${MODEL}-few_shot_${FEWSHOT}-${ACTIONS}
mkdir -p ${OUTPUT_PATH}/${domain}/${EXP_NAME}

python train.py \
    --data-path ${DATA_PATH} \
    --output-path ${OUTPUT_PATH} \
    --task-name ${TASK_PATH} \
    --test-split ${domain} \
    --exp-name ${EXP_NAME} \
    --model ${MODEL} \
    --actions ${ACTIONS} \
    --memory-path ${MEMORY_PATH} \
    --max-exec-steps 15 \
    --few-shot ${FEWSHOT} \
    --write-memory \
    --memory-type task \
    --log-print \
    --resume

Inference

Evaluation on medqa dataset with ReflecTool (Iterative Refinement) 
DATA_PATH=./ClinicalAgentBench
TASK=test

OUTPUT_PATH=./results/${TASK}

DATASET=medqa
MODEL=qwen2-7b
ACTIONS="all_wo_mm" # tool type. note that `all` and `mm` actions should use 2 gpu.
FEWSHOT=1 # num of few-shot demonstration
MEMORY_PATH=${DATA_PATH}/memory/task/long_term_memory/${DATASET}

EXP_NAME=reflectool_refine-${MODEL}-few_shot_${FEWSHOT}-${ACTIONS}-task_memory
mkdir -p ${OUTPUT_PATH}/${TASK}/${DATASET}/${EXP_NAME}

python run.py \
    --data-path ${DATA_PATH} \
    --output-path ${OUTPUT_PATH} \
    --task-name ${TASK} \
    --test-split ${DATASET} \
    --exp-name ${EXP_NAME} \
    --agent "reflectool" \
    --model ${MODEL} \
    --actions ${ACTIONS} \
    --action-guide-path ${DATA_PATH}/memory/task/tool_experience/${DATASET}.json \
    --memory-path ${MEMORY_PATH} \
    --force-action \
    --action-search "refine" \
    --few-shot ${FEWSHOT} \
    --log-print \
    --prompt-debug \
    --resume
Evaluation on medqa dataset with ReflecTool (Candidate Selection) 
DATA_PATH=./ClinicalAgentBench
TASK=test

OUTPUT_PATH=./results/${TASK}

DATASET=medqa
MODEL=qwen2-7b
ACTIONS="all_wo_mm" # tool type. note that `all` and `mm` actions should use 2 gpu.
FEWSHOT=1 # num of few-shot demonstration
MEMORY_PATH=${DATA_PATH}/memory/task/long_term_memory/${DATASET}

EXP_NAME=reflectool_select-${MODEL}-few_shot_${FEWSHOT}-${ACTIONS}-task_memory
mkdir -p ${OUTPUT_PATH}/${TASK}/${DATASET}/${EXP_NAME}

python run.py \
    --data-path ${DATA_PATH} \
    --output-path ${OUTPUT_PATH} \
    --task-name ${TASK} \
    --test-split ${DATASET} \
    --exp-name ${EXP_NAME} \
    --agent "reflectool" \
    --model ${MODEL} \
    --actions ${ACTIONS} \
    --action-guide-path ${DATA_PATH}/memory/task/tool_experience/${DATASET}.json \
    --memory-path ${MEMORY_PATH} \
    --force-action \
    --action-search "select" \
    --few-shot ${FEWSHOT} \
    --log-print \
    --prompt-debug \
    --resume

📚 Quickly Use

ReAct Agent

  • Question Answering
from reflectool.agents.TaskAgent import TaskAgent
from reflectool.commons.TaskPackage import TaskPackage
from reflectool.utilities import parse_args


args = parse_args()
args.model = "gpt-4o-mini"
Agent = TaskAgent(args)

task = TaskPackage(
    inputs="Can you explain me about the fever?",
    instruction="",
)
print(Agent(task))
  • Visual Question Answering
from reflectool.agents.TaskAgent import TaskAgent
from reflectool.commons.TaskPackage import TaskPackage
from reflectool.utilities import parse_args


args = parse_args()
args.model = "gpt-4o-mini"
Agent = TaskAgent(args)

task = TaskPackage(
    inputs="Can you descript the image?",
    instruction="",
    multimodal_inputs={
      "image": "{path to the image}",
    }
)
print(Agent(task))

ReflecTool Agent

  • Question Answering
from reflectool.agents.ReflecToolAgent import ReflecToolAgent
from reflectool.commons.TaskPackage import TaskPackage
from reflectool.utilities import parse_args


args = parse_args()
args.model = "gpt-4o-mini"
args.action_search = "refine" # verification method: "refine" or "select"
Agent = ReflecToolAgent(args)

task = TaskPackage(
    inputs="Can you explain me about the fever?",
    instruction="",
)
print(Agent(task))

🪶 Acknowledgements

Thanks to the codebase we built upon:

Citation

@misc{liao2024reflectoolreflectionawaretoolaugmentedclinical,
      title={ReflecTool: Towards Reflection-Aware Tool-Augmented Clinical Agents}, 
      author={Yusheng Liao and Shuyang Jiang and Yanfeng Wang and Yu Wang},
      year={2024},
      eprint={2410.17657},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.17657}, 
}

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Benchmark, Toolbox, and Reflection-based Method for Clinical Agent

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