README.md

Mitigating Object Hallucination via Concentric Causal Attention

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🎉 News

• [2024/11/07] Information Flow visualization is available.
• [2024/11/02] Spatial Position Probing is available on huggingface.
• [2024/10/22] Paper is available on arXiv.
• [2024/10/21] CCA-LLaVA supports evaluation of multiple benchmarks, including pope, chair, amber for hallucination, and mmstar, gqa, seed, vizwiz_vqa, scienceqa for general LVLM multiple-choice questions. Please refer to this doc for details.
• [2024/09/27] CCA is accepted to NeurIPS 2024🎉.

🕹️ Approach

• We reveal that object hallucination is closely tied with Rotary Position Encoding (RoPE), a widely adopted positional dependency modeling design in existing LVLMs. Due to the long-term decay in RoPE, LVLMs suffer from recency bias and tend to hallucinate more when relevant visual cues are distant from instruction tokens (user query) in the multimodal input sequence.
• Motivated by this, we propose Concentric Causal Attention (CCA), a simple yet effective positional alignment strategy that mitigates the impact of RoPE long-term decay in LVLMs by placing critical visual cues closer to user instructions, thereby alleviating object hallucinations.

🔥 Spatial Position Probing

• To further verify effectiveness of our approach, we craft a large-scale object hallucination evaluation set, involving over 2,000,000 testing samples that are diverse in object spatial positions and object sizes. Our model surpasses LLaVA-1.5 across diverse spatial positions and object scales consistently.

🛠️ Install

conda create -n cca-llava python=3.10 -y
conda activate cca-llava
pip install --upgrade pip  # enable PEP 660 support
pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu121
pip install -e .
pip install -e ".[train]"
pip install triton==2.1.0 pynvml==11.5.0 --upgrade
pip install flash-attn==2.5.8 --no-build-isolation --no-cache-dir

🤗 Model

• cca-llava-1.5-7b

📜 Data

Please refer to Data.md for preparation of training data.

🌟 Train

CCA-LLaVA training pipeline follows LLaVA-1.5. The training consists of two stages:

• Step 1, pretraining. Train a projector on a CC3M subset of ∼558K image-text pairs to connect a frozen pretrained vision encoder and a frozen LLM.

  bash scripts/v1_5/pretrain.cca-llava-1.5-7b.sh
  

• Step 2, instruction tuning. Fine-tune projector and LLM with ~665k multimodal instruction data.

  bash scripts/v1_5/finetune.cca-llava-1.5-7b.sh
  

🔍 Eval

Please refer to Eval.md for details.

🕹️ Usage

The two core modifications concentric positions and concentric causal masking can be found in llava/cca_utils folder. To replace default causal scheme with our proposed cca, you can prepend following code to either training or evaluation code, subject to your own use case.

import transformers
from llava.cca_utils.cca import llamaforcausallm_forward, cca_forward 
transformers.models.llama.LlamaForCausalLM.forward = llamaforcausallm_forward
transformers.models.llama.LlamaModel.forward = cca_forward

✒️ Citation

@article{xing2024mitigating,
  title={Mitigating Object Hallucination via Concentric Causal Attention},
  author={Xing, Yun and Li, Yiheng and Laptev, Ivan and Lu, Shijian},
  journal={arXiv preprint arXiv:2410.15926},
  year={2024}
}

❤️ Acknowledgement

Thanks for their wonderful work!

• llava: the codebase we use to implement cca.
• roformer: codebase where rope is initially proposed.
• opera: an excellent approach that mitigates object hallucination. the codebase we use to implement CHAIR evaluation.
• pope: a widely adopted object hallucination benchmark.
• amber: a recent comprehensive hallucination benchmark involving object, attribute and relation hallucination.
• lmms-eval: a comprehensive evaluation toolkit on LVLMs. the codebase we use to implement general LVLM benchmark evaluations.