MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
- URL: http://arxiv.org/abs/2407.04842v1
- Date: Fri, 5 Jul 2024 20:03:16 GMT
- Title: MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
- Authors: Zhaorun Chen, Yichao Du, Zichen Wen, Yiyang Zhou, Chenhang Cui, Zhenzhen Weng, Haoqin Tu, Chaoqi Wang, Zhengwei Tong, Qinglan Huang, Canyu Chen, Qinghao Ye, Zhihong Zhu, Yuqing Zhang, Jiawei Zhou, Zhuokai Zhao, Rafael Rafailov, Chelsea Finn, Huaxiu Yao,
- Abstract summary: We introduce MJ-Bench, a novel benchmark which incorporates a comprehensive preference dataset to evaluate multimodal judges.
Specifically, we evaluate a large variety of multimodal judges including smaller-sized CLIP-based scoring models, open-source VLMs, and close-source VLMs.
Experiments reveal that close-source VLMs generally provide better feedback, with GPT-4o outperforming other judges in average.
- Score: 59.7772329962047
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While text-to-image models like DALLE-3 and Stable Diffusion are rapidly proliferating, they often encounter challenges such as hallucination, bias, and the production of unsafe, low-quality output. To effectively address these issues, it is crucial to align these models with desired behaviors based on feedback from a multimodal judge. Despite their significance, current multimodal judges frequently undergo inadequate evaluation of their capabilities and limitations, potentially leading to misalignment and unsafe fine-tuning outcomes. To address this issue, we introduce MJ-Bench, a novel benchmark which incorporates a comprehensive preference dataset to evaluate multimodal judges in providing feedback for image generation models across four key perspectives: alignment, safety, image quality, and bias. Specifically, we evaluate a large variety of multimodal judges including smaller-sized CLIP-based scoring models, open-source VLMs (e.g. LLaVA family), and close-source VLMs (e.g. GPT-4o, Claude 3) on each decomposed subcategory of our preference dataset. Experiments reveal that close-source VLMs generally provide better feedback, with GPT-4o outperforming other judges in average. Compared with open-source VLMs, smaller-sized scoring models can provide better feedback regarding text-image alignment and image quality, while VLMs provide more accurate feedback regarding safety and generation bias due to their stronger reasoning capabilities. Further studies in feedback scale reveal that VLM judges can generally provide more accurate and stable feedback in natural language (Likert-scale) than numerical scales. Notably, human evaluations on end-to-end fine-tuned models using separate feedback from these multimodal judges provide similar conclusions, further confirming the effectiveness of MJ-Bench. All data, code, models are available at https://huggingface.co/MJ-Bench.
Related papers
- EVALALIGN: Supervised Fine-Tuning Multimodal LLMs with Human-Aligned Data for Evaluating Text-to-Image Models [16.18275805302776]
EvalAlign is a metric characterized by its accuracy, stability, and fine granularity.
We develop evaluation protocols that focus on two key dimensions: image faithfulness and text-image alignment.
Our comprehensive tests across 24 text-to-image generation models demonstrate that EvalAlign not only provides superior metric stability but also aligns more closely with human preferences than existing metrics.
arXiv Detail & Related papers (2024-06-24T11:56:15Z) - VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model [72.13121434085116]
VLBiasBench is a benchmark aimed at evaluating biases in Large Vision-Language Models (LVLMs)
We construct a dataset encompassing nine distinct categories of social biases, including age, disability status, gender, nationality, physical appearance, race, religion, profession, social economic status and two intersectional bias categories (race x gender, and race x social economic status)
We conduct extensive evaluations on 15 open-source models as well as one advanced closed-source model, providing some new insights into the biases revealing from these models.
arXiv Detail & Related papers (2024-06-20T10:56:59Z) - UBENCH: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions [10.28688988951815]
UBENCH is a benchmark for evaluating large language models.
It includes 3,978 multiple-choice questions covering knowledge, language, understanding, and reasoning abilities.
We also evaluate the reliability of 15 popular LLMs, finding GLM4 to be the most outstanding.
arXiv Detail & Related papers (2024-06-18T16:50:38Z) - RLAIF-V: Aligning MLLMs through Open-Source AI Feedback for Super GPT-4V Trustworthiness [94.03511733306296]
We introduce RLAIF-V, a framework that aligns MLLMs in a fully open-source paradigm for super GPT-4V trustworthiness.
RLAIF-V maximally exploits the open-source feedback from two perspectives, including high-quality feedback data and online feedback learning algorithm.
Experiments show that RLAIF-V substantially enhances the trustworthiness of models without sacrificing performance on other tasks.
arXiv Detail & Related papers (2024-05-27T14:37:01Z) - Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models [56.02275285521847]
We propose to evaluate models using a Panel of LLm evaluators (PoLL)
We find that using a PoLL composed of a larger number of smaller models outperforms a single large judge, exhibits less intra-model bias due to its composition of disjoint model families, and does so while being over seven times less expensive.
arXiv Detail & Related papers (2024-04-29T15:33:23Z) - MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large
Language Models [70.92847554971065]
We introduce MT-Eval, a comprehensive benchmark designed to evaluate multi-turn conversational abilities.
By analyzing human-LLM conversations, we categorize interaction patterns into four types: recollection, expansion, refinement, and follow-up.
Our evaluation of 11 well-known LLMs shows that while closed-source models generally surpass open-source ones, certain open-source models exceed GPT-3.5-Turbo in specific tasks.
arXiv Detail & Related papers (2024-01-30T04:50:28Z) - The False Promise of Imitating Proprietary LLMs [158.65692029352584]
An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model.
This approach looks to cheaply imitate the proprietary model's capabilities using a weaker open-source model.
We first finetune a series of LMs that imitate ChatGPT using varying base model sizes.
We then evaluate the models using crowd raters and canonical NLP benchmarks.
arXiv Detail & Related papers (2023-05-25T05:00:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.