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A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs

In this work, we present SciGraphQA, a synthetic multi-turn question-answer dataset related to academic graphs. SciGraphQA is 13 times larger than ChartVQA, the previously largest chart-visual question-answering dataset. It is also the largest open-sourced chart VQA dataset with non-synthetic charts. To build our dataset, we selected 290,000 Computer Science or Machine Learning ArXiv papers published between 2010 and 2020, and then used Palm-2 to generate 295K samples of open- vocabulary multi-turn question-answering dialogues about the graphs. As context, we provided the text-only Palm-2 with paper title, abstract, paragraph mentioning the graph, and rich text contextual data from the graph itself, obtaining dialogues with an average 2.23 question-answer turns for each graph. We asked GPT-4 to assess the matching quality of our question-answer turns given the paper’s context, obtaining an average rating of 8.7/10 on our 3K test set. We evaluated the 0-shot capability of the most popular MLLM models such as LLaVa, mPLUGowl, BLIP-2, and openFlamingo’s on our dataset, finding LLaVA-13B being the most performant with a CIDEr score of 0.08. We further enriched the question prompts for LLAVA by including the serialized data tables extracted from the graphs using the DePlot model, boosting LLaVA’s 0-shot CIDEr to 0.15. To verify the validity of our dataset, we also fine-tuned LLaVa using our dataset, reaching a substantially higher CIDEr score of 0.26. We anticipate further accuracy improvement by including segmentation mask tokens and leveraging larger LLM backbones coupled with emergent prompting techniques.

[Arxiv paper] [Training Dataset] [Test Dataset] [Model] [PaperWithCode Benchmark]

@misc{li2023scigraphqa,
  title={SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs}, 
  author={Shengzhi Li and Nima Tajbakhsh},
  year={2023},
  eprint={2308.03349},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}

Usage and License Notices: The data, code and checkpoint is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of Palm-2, LLaMA and GPT-4. If you find our work useful, please consider citing us using

Contents

• Data Download
• Install
• LLaVA-GRAPH Weights
• Serving
• Evaluation
• Fine-tuning

Data

Related datasets

Generation process

Leaderboard

Install

1. Clone this repository and navigate to LLaVA folder

git clone https://github.com/findalexli/LLaVA-Graph
cd LLaVA-Graph

2. Install Package

conda create -n llava python=3.10 -y
conda activate llava
pip install --upgrade pip  # enable PEP 660 support
pip install -e .
pip install ninja
pip install flash-attn==1.0.2

LLaVA Weights

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("alexshengzhili/LLaVa-graph-caption-to-paragraph")

LLaVA pretrained projector weights

The initial release is pretrained on [SciCapPlus] with 1 epoch. The pretrained weights are released here.

Serving

Web UI

Launch a controller

python -m llava.serve.controller --host 0.0.0.0 --port 10000

Launch a model worker

python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path alexshengzhili/LLaVa-graph-caption-to-paragraph --multi-modal

Wait until the process finishes loading the model and you see "Uvicorn running on ...".

Launch a model worker (Multiple GPUs, when GPU VRAM <= 24GB)

If your the VRAM of your GPU is less than 24GB (e.g., RTX 3090, RTX 4090, etc.), you may try running it with multiple GPUs.

python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path alexshengzhili/LLaVa-graph-caption-to-paragraph --multi-modal --num-gpus 2

Wait until the process finishes loading the model and you see "Uvicorn running on ...".

Launch a gradio web server.

python -m llava.serve.gradio_web_server --controller http://localhost:10000

You can open your browser and chat with a model now.

Code and Hyperparameters

We fine-tune the model using the code from FastChat. We use a similar set of hyperparameters as Vicuna in finetuning. Both hyperparameters used in pretraining and finetuning are provided below.

1. Pretraining

2. Finetuning

Fine-tuning with Local GPUs

LLaVA-Graph is trained on 8 A100 GPUs with 80GB memory with the following code. To train on fewer GPUs, you can reduce the per_device_train_batch_size and increase the gradient_accumulation_steps accordingly to keep the global batch size the same.

1. Pretraining

torchrun --nnodes=1 --nproc_per_node=8 --master_port=25001 \
    llava/train/train_mem.py \
    --model_name_or_path ./checkpoints/llama-vicuna-13b \
    --data_path /path/to/cc3m_595k.json \
    --image_folder /path/to/cc3m_595k \
    --vision_tower openai/clip-vit-large-patch14 \
    --tune_mm_mlp_adapter True \
    --mm_vision_select_layer -2 \
    --mm_use_im_start_end \
    --bf16 True \
    --output_dir ./checkpoints/llava-13b-pretrain \
    --num_train_epochs 1 \
    --per_device_train_batch_size 16 \
    --per_device_eval_batch_size 4 \
    --gradient_accumulation_steps 1 \
    --evaluation_strategy "no" \
    --save_strategy "steps" \
    --save_steps 2400 \
    --save_total_limit 1 \
    --learning_rate 2e-3 \
    --weight_decay 0. \
    --warmup_ratio 0.03 \
    --lr_scheduler_type "cosine" \
    --logging_steps 1 \
    --tf32 True \
    --model_max_length 2048 \
    --gradient_checkpointing True \
    --lazy_preprocess True \
    --report_to wandb

You may run this with a single A100 GPU with the following code. Please note that the per_device_train_batch_size * gradient_accumulation_steps should be equal to 128 to keep the global batch size the same.

python llava/train/train_mem.py \
    --model_name_or_path ./checkpoints/llama-vicuna-7b \
    --data_path /path/to/cc3m_595k.json \
    --image_folder /path/to/cc3m_595k \
    --vision_tower openai/clip-vit-large-patch14 \
    --tune_mm_mlp_adapter True \
    --mm_vision_select_layer -2 \
    --mm_use_im_start_end \
    --bf16 True \
    --output_dir ./checkpoints/llava-7b-pretrain \
    --num_train_epochs 1 \
    --per_device_train_batch_size 16 \
    --per_device_eval_batch_size 4 \
    --gradient_accumulation_steps 8 \
    --evaluation_strategy "no" \
    --save_strategy "steps" \
    --save_steps 2400 \
    --save_total_limit 1 \
    --learning_rate 2e-3 \
    --weight_decay 0. \
    --warmup_ratio 0.03 \
    --lr_scheduler_type "cosine" \
    --logging_steps 1 \
    --tf32 True \
    --model_max_length 2048 \
    --gradient_checkpointing True \
    --lazy_preprocess True \
    --report_to wandb

Acknowledgement

• [LLaVA] which the codebase we built on
• Vicuna: the codebase we built upon, and our base model Vicuna-13B that has the amazing language capabilities!

Related Projects

• Instruction Tuning with GPT-4
• LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
• Otter: In-Context Multi-Modal Instruction Tuning

For future project ideas, pleae check out:

• SEEM: Segment Everything Everywhere All at Once
• Grounded-Segment-Anything to detect, segment, and generate anything by marrying Grounding DINO and Segment-Anything.