Improving Visual Commonsense in Language Models via Multiple Image Generation

This repo contains the official PyTorch implementation of Improving Visual Commonsense in Language Models via Multiple Image Generation

Abstract

Commonsense reasoning is fundamentally based on multimodal knowledge. However, existing large language models (LLMs) are primarily trained using textual data only, limiting their ability to incorporate essential visual information. In contrast, Visual Language Models, which excel at visually-oriented tasks, often fail at non-visual tasks such as basic commonsense reasoning. This divergence highlights a critical challenge - the integration of robust visual understanding with foundational text-based language reasoning. To this end, we introduce a method aimed at enhancing LLMs' visual commonsense. Specifically, our method generates multiple images based on the input text prompt and integrates these into the model's decision-making process by mixing their prediction probabilities. To facilitate multimodal grounded language modeling, we employ a late-fusion layer that combines the projected visual features with the output of a pre-trained LLM conditioned on text only. This late-fusion layer enables predictions based on comprehensive image-text knowledge as well as text only when this is required. We evaluate our approach using several visual commonsense reasoning tasks together with traditional NLP tasks, including common sense reasoning and reading comprehension. Our experimental results demonstrate significant superiority over existing baselines. When applied to recent state-of-the-art LLMs (e.g., Llama3), we observe improvements not only in visual common sense but also in traditional NLP benchmarks.

Installation

git clone [email protected]:guyyariv/visually_grounded_lm.git
cd visually_grounded_lm
python -m venv vlmig
source vlmig/bin/activate
pip install -r requirements.txt

Pre-Trained Models

Download the pre-trained models provided in the paper using the following commands:

First, run pip install gdown

GPT-2

mkdir -p output/gpt2 && \
gdown "https://drive.google.com/uc?id=1ZvJTXiuXjcCCwcm_PeQ78hxZCEab4tID" -O output/gpt2/ft_wiki_laion_220_2.bin

Gemma-2B

mkdir -p output/gemma_2b && \
gdown "https://drive.google.com/uc?id=14qWLXJNMcOmgMVkQa7KAdKbcucz97_o8" -O output/gemma_2b/ft_wiki_laion_220_2.bin

LLaMA 3

mkdir -p output/llama3 && \
gdown "https://drive.google.com/uc?id=14qWLXJNMcOmgMVkQa7KAdKbcucz97_o8" -O output/llama3/ft_wiki_laion_220_2.bin

Training

Configure your Accelerate environment with:

accelerate config

Launch the training process:

accelerate launch train.py \
--run_name test \
--dataset_name cropped_vg \
--model_name_or_path meta-llama/Meta-Llama-3-8B \
--output_dir output/llama3 \
--report_to wandb \
--per_device_train_batch_size 64 \
--num_train_epochs 1 \
--run_bf16 \
--learning_rate 5e-4 \
--with_tracking

For full reproducibility, ensure to fine-tune your trained model on Wikipedia-103 (max_elements=200,000) and LAION-220.

Evaluation

We evaluate the model on multiple benchmarks:

Visual Commonsense:

For ImageNetVC evaluation (based on the official implementation https://github.com/hemingkx/ImageNetVC/blob/main/VaLM/BLIP-2/ImageNetVC.py):

python3 eval_scripts/imagenetVC.py --model_name meta-llama/Meta-Llama-3-8B --run_name llama3_imagenetvc --pretrained_model output/llama3/ft_wiki_laion_220_2 --generate_images True --k 10 

Access script parameters with:

python3 eval_scripts/imagenetVC.py --help

Commonsense:

For commonsense evaluation:

python3 eval_scripts/commonsenseQA.py --model_name meta-llama/Meta-Llama-3-8B --pretrained_model output/llama3/ft_wiki_laion_220_2 --generate_images True --k 10 --testset piqa

Note: This command example runs the testset PIQA, but this script can also be used to evaluate other datasets, such as SIQA, ARC, etc., by choosing --testset {testset name}. Access script parameters with:

python3 eval_scripts/commonsenseQA.py --help

Reading Comprehension

For SQuAD, QUAC, and BoolQ run respectively:

python3 eval_scripts/squad.py --model_name meta-llama/Meta-Llama-3-8B --pretrained_model output/llama3/ft_wiki_laion_220_2 --generate_images True --k 10
python3 eval_scripts/quac.py --model_name meta-llama/Meta-Llama-3-8B --pretrained_model output/llama3/ft_wiki_laion_220_2 --generate_images True --k 10
python3 eval_scripts/boolq.py --model_name meta-llama/Meta-Llama-3-8B --pretrained_model output/llama3/ft_wiki_laion_220_2 --generate_images True --k 10

Access script parameters with:

python3 eval_scripts/squad.py --help
python3 eval_scripts/quac.py --help
python3 eval_scripts/boolq.py --help

Acknowledgments

Our code it partially built upon Transformers training example script and ImagenetVC

Cite

If you use our work in your research, please cite the following paper:

@misc{yariv2024improving,
      title={Improving Visual Commonsense in Language Models via Multiple Image Generation}, 
      author={Guy Yariv and Idan Schwartz and Yossi Adi and Sagie Benaim},
      year={2024},
      eprint={2406.13621},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

License

This repository is released under the MIT license as found in the LICENSE file.