From 19e686b23a7e94a15d4e0f5209ac26966eacef0b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=B0=8F=E8=87=A3=E5=AD=90=E5=90=83=E5=A4=A7=E6=A9=99?= =?UTF-8?q?=E5=AD=90?= Date: Thu, 22 Feb 2024 15:10:05 +0800 Subject: [PATCH] update README.md --- README.md | 118 +++++++++++++++--- ...mark_v1.0.pdf => MULTI_Benchmark_v1.3.pdf} | Bin 2702650 -> 2764491 bytes eval/args.py | 18 +-- eval/models/README.md | 17 ++- 4 files changed, 122 insertions(+), 31 deletions(-) rename docs/static/pdfs/{MULTI_Benchmark_v1.0.pdf => MULTI_Benchmark_v1.3.pdf} (79%) diff --git a/README.md b/README.md index a2eb9f5..a8fdd1e 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ ![MULTI](./docs/static/images/overview.png) -🌐 [Website](https://OpenDFM.github.io/MULTI-Benchmark/) | 📃 [Paper](https://arxiv.org/abs/2402.03173/) | 🤗 [Dataset](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark) | 🎯 [Leaderboard]() (Coming Soon) +🌐 [Website](https://OpenDFM.github.io/MULTI-Benchmark/) | 📃 [Paper](https://arxiv.org/abs/2402.03173/) | 🤗 [Dataset](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark) | 🎯 [Leaderboard]() (Coming Soon) [简体中文](./README_zh.md) | English @@ -20,41 +20,130 @@ ## 📖 Overview -Rapid progress in multimodal large language models (MLLMs) highlights the need to introduce challenging yet realistic benchmarks to the academic community, while existing benchmarks primarily focus on understanding simple natural images and short context. In this paper, we present ***MULTI***, as a cutting-edge benchmark for evaluating MLLMs on understanding complex tables and images, and reasoning with long context. **MULTI** provides multimodal inputs and requires responses that are either precise or open-ended, reflecting real-life examination styles. **MULTI** includes over 18,000 questions and challenges MLLMs with a variety of tasks, ranging from formula derivation to image detail analysis and cross-modality reasoning. We also introduce ***MULTI-Elite***, a 500-question selected hard subset, and ***MULTI-Extend***, with more than 4,500 external knowledge context pieces. Our evaluation indicates significant potential for MLLM advancement, with GPT-4V achieving a **63.7%** accuracy rate on **MULTI**, in contrast to other MLLMs scoring between **28.5%** and **55.3%**. **MULTI** serves not only as a robust evaluation platform but also paves the way for the development of expert-level AI. +Rapid progress in multimodal large language models (MLLMs) highlights the need to introduce challenging yet realistic benchmarks to the academic community, while existing benchmarks primarily focus on understanding simple natural images and short context. In this paper, we present***MULTI***, as a cutting-edge benchmark for evaluating MLLMs on understanding complex tables and images, and reasoning with long context. **MULTI** provides multimodal inputs and requires responses that are either precise or open-ended, reflecting real-life examination styles. **MULTI** includes over 18,000 questions and challenges MLLMs with a variety of tasks, ranging from formula derivation to image detail analysis and cross-modality reasoning. We also introduce***MULTI-Elite***, a 500-question selected hard subset, and ***MULTI-Extend***, with more than 4,500 external knowledge context pieces. Our evaluation indicates significant potential for MLLM advancement, with GPT-4V achieving a **63.7%** accuracy rate on **MULTI**, in contrast to other MLLMs scoring between **28.5%** and **55.3%**. **MULTI** serves not only as a robust evaluation platform but also paves the way for the development of expert-level AI. ## 🏆 Leaderboard | Modality | Model | Version | Overall | MULTI-Elite | -|:--------:|:-------------:| -------------------------- |:-------:|:-----------:| -| 🖼️ | GPT-4V | gpt-4-vision-preview | 63.7 | 14.0 | -| 🖼️ | Yi-VL | Yi-34B-Chat | 55.3 | 26.2 | -| 🖼️ | Gemini Vision | gemini-pro-vision | 53.7 | 12.4 | +|:--------:|:-------------:|----------------------------|:-------:|:-----------:| +| 🖼️ | GPT-4V | gpt-4-vision-preview | 63.7 | 14.0 | +| 🖼️ | Yi-VL | Yi-34B-Chat | 55.3 | 26.2 | +| 🖼️ | Gemini Vision | gemini-pro-vision | 53.7 | 12.4 | | 📃 | Gemini | gemini-pro | 52.2 | 10.5 | | 📃 | GPT-4 | gpt-4-1106-preview | 50.2 | 5.8 | | 📃 | DFM-2.0 | dfm-2.0-70b-preview | 49.7 | 18.0 | -| 🖼️ | InternVL | InternVL-Chat-Chinese-V1.1 | 44.9 | 20.7 | -| 🖼️ | Qwen-VL | Qwen-VL-Chat | 39.0 | 10.5 | +| 🖼️ | InternVL | InternVL-Chat-Chinese-V1.1 | 44.9 | 20.7 | +| 🖼️ | Qwen-VL | Qwen-VL-Chat | 39.0 | 10.5 | | 📃 | ChatGPT | gpt-3.5-turbo-1106 | 35.9 | 4.7 | -| 🖼️ | VisCPM | VisCPM-Chat | 33.4 | 13.0 | +| 🖼️ | VisCPM | VisCPM-Chat | 33.4 | 13.0 | | 📃 | MOSS | moss-moon-003-sft | 32.6 | 13.1 | -| 🖼️ | VisualGLM | visualglm-6b | 31.1 | 12.8 | -| 🖼️ | Chinese-LLaVA | Chinese-LLaVA-Cllama2 | 28.5 | 12.3 | +| 🖼️ | VisualGLM | visualglm-6b | 31.1 | 12.8 | +| 🖼️ | Chinese-LLaVA | Chinese-LLaVA-Cllama2 | 28.5 | 12.3 | For more details, please visit our [leaderboard]() (Coming Soon). ## ⏬ Download -You can download the dataset from the [HuggingFace Page](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark). Current [version](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark/blob/main/MULTI_v1.2.2_20240212_release.zip) is `v1.2.2`. +You can download the dataset from the [HuggingFace Page](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark). Current [version](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark/blob/main/MULTI_v1.2.2_20240212_release.zip) is `v1.2.2`. Unzip the files and put them under `data/`. ``` wget https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark/resolve/main/MULTI_v1.2.2_20240212_release.zip -unzip MULTI_v1.2.2_20240212_release.zip -d ./data +unzip MULTI_v1.2.2_20240212_release.zip -d ./data/ +``` + +The structure of `data/` should be something like: + +``` +data +├── images # folder containing images +├── problem_v1.2.2_20240212_release.json # MULTI +├── knowledge_v1.2.2_20240212_release.json # MULTI-Extend +├── hard_list_v1.2.1_20240206.json # MULTI-Elite +└── captions_v1.2.0_20231217.csv # image captions generated by BLIP-6.7b ``` ## 📝 How to Evaluate This will be updated soon. Please refer to the [legancy README](./eval/models/README.md) for now. +We provide a unified evaluation framework to test the performance of different models on our benchmark. The code is located in the `eval` folder. Each file contains an evaluator specified to one LLM, and implements a `generate_answer` method to receive a question as input and give out the answer of it. + +```shell +cd eval +python eval.py -h # to list all supported arguments +python eval.py -l # to list all supported models +``` + +### Environment Preparation before Usage + +Each evaluator requires its unique environment setting, and a universal environment may not work for all the evaluators. **Just follow the official guide.** If the corresponding model runs well, then so should it fit in our framework. + +You just need to install another two packages to run the evaluation code: + +```shell +pip install tiktoken tqdm +``` + +If you just want to generate data for a specific setting (using `--debug` argument), this line above is all you need. + +### Running Evaluation + +For a quick start, see these examples as reference: + +Test GPT-4V model on whole MULTI with multimodal input, using MULTI-Extend as external knowledge: + +```shell +python eval.py \ + --problem_file ../data/problem_v1.2.2_20240212_release.json \ + --knowledge_file ../data/knowledge_v1.2.2_20240212_release.json \ + --questions_type 0,1,2,3 \ + --image_type 0,1,2 \ + --input_type 2 \ + --model gpt-4v \ + --model_version gpt-4-vision-preview \ + --api_key sk-************************************************ +``` + +Test Qwen-VL model on MULTI-Elite with image caption input, skip all questions not containing images, evaluate only multiple-choice questions, automatically set cuda device: + +```shell +python eval.py \ + --problem_file ../data/problem_v1.2.2_20240212_release.json \ + --subset ../data/hard_list_v1.2.1_20240206.json \ + --caption_file ../data/captions_v1.2.0_20231217.csv \ + --questions_type 0,1 \ + --image_type 1,2 \ + --input_type 1 \ + --model qwen-vl \ + --model_dir ../models/Qwen-VL-Chat +``` + +The evaluation script will generate a folder named `results` under the working directory, and the result will be saved in `results/EXPERIMENT_NAME`. During the evaluation, the script will save checkpoints in `results/EXPERIMENT_NAME/checkpoints`, you can delete them after the evaluation is done. If the evaluation is interrupted, you can continue from the last checkpoint: + +```shell +python eval.py \ + --checkpoint_dir ../results/EXPERIMENT_NAME +``` + +Most of the arguments are saved in `../results/EXPERIMENT_NAME/args.json`, so you can continue the evaluation without specifying all the arguments again. Note that `--api_key` is not saved in `args.json` for security reasons, so you need to specify it again. + +```shell +python eval.py \ + --checkpoint_dir ../results/EXPERIMENT_NAME \ + --api_key sk-************************************************ +``` + +### Implement Support for Your Models + +It's recommended to read the code of the given evaluators before your implementation, implement `generate_answer(self, question:dict)` to match the design supported in `eval.py` and `eval.sh`, which is anticipated to largely ease the coding process. +**Do not forget to add their references into `../args.py` for the convenience of usage.** + +You can execute `model_tester.py` in the parent folder to check the correctness of you implementation. Various problems including implementation errors, small bugs in code, and even wrong environment setting may cause failure of the evaluation. The examples provided in the file cover most kinds of cases presented in our benchmark. Feel free to change the code in it to debug your code😊 + +```shell +python model_tester.py # args are similar to the default settings above +``` + ## 📮 How to Submit You need to first prepare a UTF-8 encoded JSON file with the following format: @@ -71,7 +160,8 @@ You need to first prepare a UTF-8 encoded JSON file with the following format: ... } ``` -If you evaluate the model with our official code, you can simply zip the experiment result folder `./results/EXPERIMENT_NAME`. + +If you evaluate the model with our official code, you can simply zip the experiment result folder `./results/EXPERIMENT_NAME`. Then, you can submit your result to our [evaluation platform](https://wj.sjtu.edu.cn/q/89UmRAJn) (Coming Soon). diff --git a/docs/static/pdfs/MULTI_Benchmark_v1.0.pdf b/docs/static/pdfs/MULTI_Benchmark_v1.3.pdf similarity index 79% rename from docs/static/pdfs/MULTI_Benchmark_v1.0.pdf rename to docs/static/pdfs/MULTI_Benchmark_v1.3.pdf index 2e7a5f4aab2967d5dcb5bc080988590272f3d3a2..4a27e2eda22c84a33a460462a241114f84ab46d4 100644 GIT binary patch delta 606377 zcmX`RQ+S|F&@DQ#olHEjZQHgdwr#u<+qP}nn%K7OiFLkzpZ)Kl~YimGCo^ipnE0xI!8w2oI zU(&X<-;hN7y4GhRs4hr3(JdsVG|;)%v#)L2s`lq0r2AQZK)NlrZ?pe<0{{-Yq*BD? z=eOP+C~-ClQ9?zW{)QgPnm@l^4X=4A$jg^Bety~bHgi#wE-YQg$ewh5`nijge6Rk# z9Q&7gb+nY@lQDhLS$mj-D&2sP9~}-!8AcJx{!E9b9pFzPsts{k3?T zfk^MsS2O~coVd<=eS5KWqt2giH^0*9TT^47aSzWw_jScQ@*XALwt#pYLfHr9lv~UY zdIkkm+0GX&Il(cW+YYej))kf*8g?{_#HAFT8M;%wE+HMdc>rs<#1^88LN_u#r zLrEt)gVnxKbnjdjzFwWmiB7k-*eK<>-C6y62s_5pJVw<7nNRq*2fIAvxc~0^UOEM{ z>7%|pne$=G_(I1Z4;-K@^X|~z<5$NlgqfmRGHE9Nn!r>}fKjILU;B+c7KTcGB8=0} 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pNWZnu9Gj34Pq{#NVgzs04a*%pETdl-mJDLa8CJP+<2I>b{|D|e>NWrX diff --git a/eval/args.py b/eval/args.py index f1856c9..f7ab397 100644 --- a/eval/args.py +++ b/eval/args.py @@ -105,7 +105,7 @@ def parse_args_for_eval(): parser.add_argument('--model', '-m', type=str, default=None, help='Specify the model name.') parser.add_argument('--model_version', '-v', type=str, default=None, help='Specify the model type. You need to fill in this if you want to test specific model version for GPTs.') parser.add_argument('--model_dir', '-d', type=str, default=None, help='Specify the model directory. You need to fill in this if you want to test those models that are deployed locally.') - parser.add_argument('--cuda_device', '-c', type=str, default='cpu', help='Specify the cuda device. By leaving it empty, it means using cpu. You can specify multiple cuda devices by separating them with commas, i.e. "0,1,2,3".') + parser.add_argument('--cuda_device', '-c', type=str, default='auto', help='Specify the cuda device. By leaving it empty, it means using auto. You can specify multiple cuda devices by separating them with commas, i.e. "0,1,2,3".') parser.add_argument('--api_key', type=str, default=None, help='Specify the api key. You need to fill in this if you want to test those models that are not deployed locally.') parser.add_argument('--api_url', type=str, default="https://api.openai.com/v1/chat/completions", help='Specify the api url. You need to fill in this if you want to test those models that are not deployed locally.') @@ -184,11 +184,13 @@ class Args: def print_model_list(): + print('='*20) for model_name in model_list: - print(model_name) - print(model_list[model_name]) - versions = model_list[model_name].get("avail_model", []) - if len(versions) > 0: - print(f"Available versions: {versions}") - print() - print(model_list) + print(f'[{model_name}]') + print(' ',model_list[model_name]) + # versions = model_list[model_name].get("avail_model", []) + # if len(versions) > 0: + # print(f"Available versions: {versions}") + # print() + # print(model_list) + print('=' * 20) diff --git a/eval/models/README.md b/eval/models/README.md index a0fdc4e..4070e94 100644 --- a/eval/models/README.md +++ b/eval/models/README.md @@ -6,28 +6,27 @@ This folder contains all evaluators to support LLM performance test. Each file c ## Environment preparation before usage -Each evaluator requires its unique environment setting, and a universal environment may not work for all the evaluators. Follow the official guide If the corresponding model can run well, then so should it fit in our framework. +Each evaluator requires its unique environment setting, and a universal environment may not work for all the evaluators. Follow the official guide. If the corresponding model can run well, then so should it fit in our framework. ### Properties for the implemented models -Refer to our [paper](), section 4.2. +Refer to our [paper](../../docs/static/pdfs/MULTI_Benchmark_v1.3.pdf), section 4.2. ## How to run a test ### Dataset preparation -First, get our dataset from huggingface at [here](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark/). Unzip the files and put them under `data/`. +First, get our dataset from [huggingface](https://huggingface.co/datasets/OpenDFM/MULTI-Benchmark/). Unzip the files and put them under `data/`. The structure of `data` should be something like: ``` data -|- images -|- problem_v1.2.2_20240212_release.json -|- knowledge_v1.2.2_20240212_release.json -|- hard_list_v1.2.1_20240206.json -|- captions_v1.2.0_20231217.csv - +├── images # images folder +├── problem_v1.2.2_20240212_release.json # MULTI +├── knowledge_v1.2.2_20240212_release.json # MULTI-Extend +├── hard_list_v1.2.1_20240206.json # MULTI-Elite +└── captions_v1.2.0_20231217.csv # image captions generated by BLIP-6.7b ``` ### Arguments for test