Data-Augmented Phrase-Level Alignment for Mitigating Object Hallucination

ICLR 2025

Pritam Sarkar   Sayna Ebrahimi   Ali Etemad  
Ahmad Beirami   Sercan O Arik   Tomas Pfister

[arXiV] [OpenReview] [GitHub] [Model Weights 🤗] [Training Data]

Please see our [GitHUb](https://github.com/pritamqu/HALVA/) repo for details. ### Setup environment ``` conda create -n halva python=3.10 -y conda activate halva pip install --upgrade pip pip install -r req.txt module load cuda/11.7.1 pip install flash-attn --no-build-isolation ``` ### Try HALVA! We share a minimal setup to quickly try our HALVA! See this [notebook](https://github.com/pritamqu/HALVA/blob/master/try_halva.ipynb). ### Model weights - [HALVA 7B](https://huggingface.co/pritamqu/halva7b-lora) - [HALVA 13B](https://huggingface.co/pritamqu/halva13b-lora) - [HALVA 13B/384](https://huggingface.co/pritamqu/halva13b384-lora) ### Training HALVA ### Data **generative data augmented contrastive samples** - Vision-language instructions and their correct and hallucinated responses are available here: [data](https://github.com/pritamqu/HALVA/blob/master/data/data.json) - Download the images from Visual Genome and save both part 1 and part 2 as `data/vg/VG_100K` and `data/vg/VG_100K_2` **reference samples** - A random subset from [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/tree/main). For reproducibility, we share the actual subset that has been used in our study: [ref data](data/ref_data.json) - Image sources: - MSCOCO - download them as `data/MSCOCO2017` - TextVQA - download them as `data/textvqa` - GQA - download them as `data/gqa` - OCR-VQA - download them as `data/ocr_vqa` ### Train - The base model LLaVA-v1.5 weights can be found here: [7B](https://huggingface.co/liuhaotian/llava-v1.5-7b) and [13B](https://huggingface.co/liuhaotian/llava-v1.5-13b). - We use 4-A100 80GB GPUs for training, which takes 1.5 hours and 3 hours for training 7B and 13B variants, respectively. If you are using different GPUs, please make sure to match our default batch_size x gradient accumulation steps, for optimal performance with the default hyperparameters. - The following training script can be used to train HALVA that uses LLaVA 1.5 as the base model: - HALVA-7B: `src/hallava_7b.sh` - HALVA-13B: `src/hallava_13b.sh` ### Evaluation on hallucination benchmarks Choose the HALVA variant and their base model. We provide sample validation scripts for evaluation, **please make sure to update the paths based on your setup**. ``` MODEL="halva13b-lora" MODEL_BASE="liuhaotian/llava-v1.5-13b" # OR MODEL="halva7b-lora" MODEL_BASE="liuhaotian/llava-v1.5-7b" ``` #### CHAIR - Download the validation images from [MSCOCO2014](https://cocodataset.org/#download) and store them as `data/MSCOCO2014/val2014`. We use the same 500 images for validation, as used in [prior work](https://github.com/yuezih/less-is-more/blob/main/CHAIR-eval/data/chair-500.jsonl). - You can use the given sample script for evaluation. ``` ##### run chair bash src/evaluate_hall/chair.sh ${MODEL} ${MODEL_BASE} ``` #### MME-Hall - MME-Hall is a subset of MME consisting of `existence`, `count`, `position`, and `color`. - You can follow the official instructions for MME evaluation: [link](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation) and download the MME benchmark. - Once the data is downloaded you can use the given sample script for evaluation. ``` ##### run mme bash src/evaluate_hall/mme.sh ${MODEL} ${MODEL_BASE} ``` #### AMBER - Download the validation images are from the source repo [AMBER](https://github.com/junyangwang0410/AMBER/tree/master) and keep them as `data/amber/image/`. - Download the annotation [data](https://github.com/junyangwang0410/AMBER/tree/master/data) directory and save as `eval_hall/amber/data`. - Once the data is downloaded you can use the given sample script for evaluation. ``` ##### run amber evaluation on 4 GPUs in parallel if available, else run sequentially by removing & from the end bash src/evaluate_hall/amber.sh g ${MODEL} ${MODEL_BASE} 0 & bash src/evaluate_hall/amber.sh da ${MODEL} ${MODEL_BASE} 1 & bash src/evaluate_hall/amber.sh dr ${MODEL} ${MODEL_BASE} 2 & bash src/evaluate_hall/amber.sh de ${MODEL} ${MODEL_BASE} 3 & wait # get amber f1 for all discriminative tasks bash src/evaluate_hall/amber_f1.sh ${MODEL} ``` #### MMHal-Bench - The validation data will be directly downloaded from HuggingFace. You can use the given sample script for evaluation. ``` ##### run mmhal-bench bash src/evaluate_hall/mmhal.sh ${MODEL} ${MODEL_BASE} 0 ``` #### HallusionBench - Download the validation images from [link](https://drive.google.com/file/d/1eeO1i0G9BSZTE1yd5XeFwmrbe1hwyf_0/view?usp=sharing) and save them in `data/hallusion_bench`. - Download the annotation files from [link](https://github.com/tianyi-lab/HallusionBench/blob/main/HallusionBench.json) and save them in `eval_hall/hallusion_bench`. - For more details, you can check the [official repo](https://github.com/tianyi-lab/HallusionBench). You can use the given sample script for evaluation. ``` ##### run halusion-bench bash src/evaluate_hall/hallusionbench.sh ${MODEL} ${MODEL_BASE} 0 ``` ### Evaluation on general vision-language tasks In addition to the above-mentioned evaluation on hallucination benchmarks, we also evaluate on general vision-language benchmarks. For those, we directly follow LLaVA repo as follows: - [VQA](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md#vqav2) - [MM-Vet](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md#mm-vet) - [TextVQA](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md#textvqa) - [MME](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md#mme) ### VILA The above instructions are mainly related to the LLaVA 1.5 based checkpoints, you can find the VILA codes inside `*_vila` directories. ### Citation If you find this repository useful, please consider giving a star :star: and citation using the given BibTeX entry: ``` @misc{sarkar2024halva, title={Data-Augmented Phrase-Level Alignment for Mitigating Object Hallucination}, author={Pritam Sarkar and Sayna Ebrahimi and Ali Etemad and Ahmad Beirami and Sercan Ö. Arık and Tomas Pfister}, year={2024}, eprint={2405.18654}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ### Acknowledgement This code base is built upon [LLaVA](https://github.com/haotian-liu/LLaVA/tree/main) and [VILA](https://github.com/NVlabs/VILA).