Elamur Research⌕
☰
home / papers / 2024 / VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation

VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation

Paper · 2024 · Max Ku, Dongfu Jiang, Cong Wei et al.

Summary

Judging images from conditional generators is difficult. Standard automatic metrics such as CLIP-Score, LPIPS or DreamSim each measure one fixed property, ignore the target task, and return an unexplained number. Human evaluation is more reliable but expensive and subjective. The authors propose VIEScore, which asks a Multimodal Large Language Model (MLLM) to evaluate a synthetic image. Given a task instruction and the relevant conditions, the model writes a natural-language justification and then gives a score. No training or fine-tuning is needed.

They test several MLLM backbones (GPT-4o, GPT-4v, Gemini, LLaVA and other open models) on seven tasks, using human ratings from ImagenHub. Ratings cover semantic consistency (SC), perceptual quality (PQ) and an overall score.

With GPT-4o as the backbone, VIEScore reaches roughly the same agreement with people as annotators reach with each other. Open-source backbones fall well behind, and editing tasks remain the hardest. The work suggests that explainable, task-adaptable MLLM judges could take over much of the human rating effort in image synthesis research.

Key points

  • With GPT-4o, VIEScore reaches a Spearman correlation of about 0.4 with human scores, against roughly 0.45 between human raters.
  • The seven evaluation tasks cover:
    • text-to-image generation;
    • text-guided editing and mask-guided editing (inpainting);
    • subject-driven generation and editing;
    • multi-concept composition;
    • control-guided generation.
  • The human ratings come from ImagenHub, whose raters agree at about 0.4 Krippendorff's alpha. PQ correlations are lower than SC correlations, for humans and MLLMs alike.
  • Traditional metrics win in some places:
    • DINO beats GPT-4v on subject-driven tasks.
    • LPIPS beats GPT-4v, Gemini and LLaVA on control-guided generation.
    • GPT-4o still comes out highest overall.
    • CLIP-Score correlates weakly with humans and never exceeded 0.3 on any image.
  • GPT-4v's ranking of models correlates positively with the ImagenHub leaderboard on every task. The GPT-4v and GPT-4o experiments cost about 500 USD in API calls.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksComputer Vision and Pattern Recognition · 2,869 works

Cite

Cite as Max Ku, Dongfu Jiang, Cong Wei, Xiang Yue and Wenhu Chen. 2024. VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.acl-long.663

@inproceedings{ku2024viescore,
  title = {{VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation}},
  author = {Max Ku, Dongfu Jiang, Cong Wei, Xiang Yue and Wenhu Chen},
  booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2024},
  doi = {10.18653/v1/2024.acl-long.663},
  url = {https://doi.org/10.18653/v1/2024.acl-long.663}
}

References

76 references, 25 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Omri Avrahami, Dani Lischinski, and Ohad Fried. 2022. Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18208–18218. find
  2. Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, and Ludwig Schmidt. 2023. Openflamingo: An open-source framework for training…
  3. Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023. Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond. arXiv preprint arXiv:2308.12966.
  4. Omer Bar-Tal, Dolev Ofri-Amar, Rafail Fridman, Yoni Kasten, and Tali Dekel. 2022. Text2live: Text-driven layered image and video editing. In European Conference on Computer Vision, pages 707–723. find
  5. Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sağnak Taşırlar. 2023. Introducing our multimodal models. find
  6. Tim Brooks, Aleksander Holynski, and Alexei A. Efros. 2023. Instructpix2pix: Learning to follow image editing instructions. In CVPR. find
  7. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, C… — Language Models are Few-Shot Learners
  8. Mathilde Caron, Hugo Touvron, Ishan Misra, Herv’e J’egou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. 2021. Emerging properties in self-supervised vision transformers. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 9630– 9640.
  9. Wenhu Chen, Hexiang Hu, Yandong Li, Nataniel Rui, Xuhui Jia, Ming-Wei Chang, and William W Cohen. 2023. Subject-driven text-to-image generation via apprenticeship learning. arXiv preprint arXiv:2304.00186. — [arXiv:2304.00186](https://arxiv.org/abs/2304.00186) find
  10. Jaemin Cho, Yushi Hu, Roopal Garg, Peter Anderson, Ranjay Krishna, Jason Baldridge, Mohit Bansal, Jordi Pont-Tuset, and Su Wang. 2023. Davidsonian scene graph: Improving reliability in fine-grained evaluation for text-image generation. arXiv preprint arXiv:2310.18235. find
  11. Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord. 2022. Diffedit: Diffusion-based semantic image editing with mask guidance. In The Eleventh International Conference on Learning Representations. find
  12. Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Albert Li, Pascale Fung, and Steven C. H. Hoi. 2023. Instructblip: Towards general-purpose vision-language models with instruction tuning. ArXiv, abs/2305.06500. deep floyd.ai. 2023. If by deepfloyd lab at…
  13. Emily L Denton, Soumith Chintala, Rob Fergus, et al. 2015. Deep generative image models using a laplacian pyramid of adversarial networks. Advances in neural information processing systems, 28. find
  14. Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat gans on image synthesis. In Advances in Neural Information Processing Systems, volume 34, pages 8780–8794. Curran Associates, Inc. find
  15. Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Alpacafarm: A simulation framework for methods that learn from human feedback.
  16. Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu. 2023a. Gptscore: Evaluate as you desire. ArXiv, abs/2302.04166. find
  17. Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. 2023b. Dreamsim: Learning new dimensions of human visual similarity using synthetic data. find
  18. Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit Haim Bermano, Gal Chechik, and Daniel Cohen-or. 2022. An image is worth one word: Personalizing text-to-image generation using textual inversion. In The Eleventh International Conference on Learning Representations. find
  19. Mert Inan, Piyush Sharma, Baber Khalid, Radu Soricut, Matthew Stone, and Malihe Alikhani. 2021. COS-Mic: A coherence-aware generation metric for image descriptions. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3419– 3430, Punta Cana, Dominican Republic. Association… find
  20. Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2017. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1125–1134. find
  21. Dongfu Jiang, Yishan Li, Ge Zhang, Wenhao Huang, Bill Yuchen Lin, and Wenhu Chen. 2023. Tigerscore: Towards building explainable metric for all text generation tasks. arXiv preprint arXiv:2310.00752.
  22. Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. 2016. Deep learning, volume 1. MIT Press. find
  23. Jin-Hwa Kim, Yunji Kim, Jiyoung Lee, Kang Min Yoo, and Sang-Woo Lee. 2022. Mutual information divergence: A unified metric for multimodal generative models. Advances in Neural Information Processing Systems, 35:35072–35086. find
  24. Jing Gu, Yilin Wang, Nanxuan Zhao, Tsu-Jui Fu, Wei Xiong, Qing Liu, Zhifei Zhang, He Zhang, Jianming Zhang, HyunJoon Jung, et al. 2023. Photoswap: Personalized subject swapping in images. arXiv preprint arXiv:2305.18286. — [arXiv:2305.18286](https://arxiv.org/abs/2305.18286) find
  25. Younghyun Kim, Sangwoo Mo, Min-Kyung Kim, Kyungmin Lee, Jaeho Lee, and Jinwoo Shin. 2023. Bias-to-text: Debiasing unknown visual biases through language interpretation. ArXiv. find
  26. Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. 2021. Clipscore: A reference-free evaluation metric for image captioning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7514–7528.
  27. Max Ku, Tianle Li, Kai Zhang, Yujie Lu, Xingyu Fu, Wenwen Zhuang, and Wenhu Chen. 2023. Imagenhub: Standardizing the evaluation of conditional image generation models. arXiv preprint arXiv:2310.01596. — [arXiv:2310.01596](https://arxiv.org/abs/2310.01596) find
  28. Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30. find
  29. Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. 2023. Multi-concept customization of text-to-image diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1931–1941. find
  30. Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, volume 33, pages 6840–6851. Curran Associates, Inc. find
  31. Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila. 2019. Improved precision and recall metric for assessing generative models. Advances in Neural Information Processing Systems, 32. find
  32. Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2021. Lora: Low-rank adaptation of large language models. In International Conference on Learning Representations.
  33. Hwanhee Lee, Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, and Kyomin Jung. 2020. ViLBERTScore: Evaluating image caption using vision-and-language BERT. In Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, pages 34–39, Online. Association for Computational… find
  34. Yushi Hu, Benlin Liu, Jungo Kasai, Yizhong Wang, Mari Ostendorf, Ranjay Krishna, and Noah A Smith. 2023. Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering. arXiv preprint arXiv:2303.11897. find
  35. Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. 2023. T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation. ArXiv, abs/2307.06350. find
  36. Tony Lee, Michihiro Yasunaga, Chenlin Meng, Yifan Mai, Joon Sung Park, Agrim Gupta, Yunzhi Zhang, Deepak Narayanan, Hannah Benita Teufel, Marco Bellagente, et al. 2023. Holistic evaluation of text-to-image models. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Ben… find
  37. Dongxu Li, Junnan Li, and Steven CH Hoi. 2023a. Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing. arXiv preprint arXiv:2305.14720. — [arXiv:2305.14720](https://arxiv.org/abs/2305.14720) find
  38. Junlong Li, Shichao Sun, Weizhe Yuan, Run-Ze Fan, Hai Zhao, and Pengfei Liu. 2023b. Generative judge for evaluating alignment. arXiv preprint arXiv:2310.05470.
  39. Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, pages 12888–12900. PMLR.
  40. Tianle Li, Max Ku, Cong Wei, and Wenhu Chen. 2023c. Dreamedit: Subject-driven image editing. arXiv preprint arXiv:2306.12624. — [arXiv:2306.12624](https://arxiv.org/abs/2306.12624) find
  41. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023a. Visual instruction tuning. ArXiv, abs/2304.08485.
  42. Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021. What makes good in-context examples for gpt-3? In Workshop on Knowledge Extraction and Integration for Deep Learning Architectures; Deep Learning Inside Out.
  43. Zhiheng Liu, Yifei Zhang, Yujun Shen, Kecheng Zheng, Kai Zhu, Ruili Feng, Yu Liu, Deli Zhao, Jingren Zhou, and Yang Cao. 2023b. Cones 2: Customizable image synthesis with multiple subjects. arXiv preprint arXiv:2305.19327. — [arXiv:2305.19327](https://arxiv.org/abs/2305.19327) find
  44. Lingxiao Lu, Bo Zhang, and Li Niu. 2023a. Dreamcom: Finetuning text-guided inpainting model for image composition. ArXiv, abs/2309.15508. — [arXiv:2309.15508](https://arxiv.org/abs/2309.15508) find
  45. Yujie Lu, Xiujun Li, William Yang Wang, and Yejin Choi. 2023b. Vim: Probing multimodal large language models for visual embedded instruction following. ArXiv, abs/2311.17647. — [arXiv:2311.17647](https://arxiv.org/abs/2311.17647) find
  46. Yujie Lu, Xianjun Yang, Xiujun Li, Xin Eric Wang, and William Yang Wang. 2023c. Llmscore: Unveiling the power of large language models in text-to-image synthesis evaluation. arXiv preprint arXiv:2305.11116. — [arXiv:2305.11116](https://arxiv.org/abs/2305.11116) find
  47. Yujie Lu, Xianjun Yang, Xiujun Li, Xin Eric Wang, and William Yang Wang. 2023d. Llmscore: Unveiling the power of large language models in text-to-image synthesis evaluation. ArXiv, abs/2305.11116. — [arXiv:2305.11116](https://arxiv.org/abs/2305.11116) find
  48. Andreas Lugmayr, Martin Danelljan, Andrés Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. 2022. Repaint: Inpainting using denoising diffusion probabilistic models. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11451–11461. find
  49. Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. 2021. Sdedit: Guided image synthesis and editing with stochastic differential equations. In International Conference on Learning Representations. find
  50. Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. 2023. Null-text inversion for editing real images using guided diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6038– 6047. find
  51. OpenAI. 2023. Gpt-4 technical report. openjourney.ai. 2023. Openjourney is an open source stable diffusion fine tuned model on midjourney images.
  52. Dong Huk Park, Samaneh Azadi, Xihui Liu, Trevor Darrell, and Anna Rohrbach. 2021. Benchmark for compositional text-to-image synthesis. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1). find
  53. Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu. 2023. Zero-shot image-to-image translation. In ACM SIG-GRAPH 2023 Conference Proceedings, pages 1–11. find
  54. Archit Parnami and Minwoo Lee. 2022. Learning from few examples: A summary of approaches to few-shot learning. ArXiv, abs/2203.04291. — [arXiv:2203.04291](https://arxiv.org/abs/2203.04291) find
  55. Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, and Furu Wei. 2023. Kosmos-2: Grounding multimodal large language models to the world. ArXiv, abs/2306.14824.
  56. Can Qin, Shu Zhang, Ning Yu, Yihao Feng, Xinyi Yang, Yingbo Zhou, Huan Wang, Juan Carlos Niebles, Caiming Xiong, Silvio Savarese, et al. 2023. Unicontrol: A unified diffusion model for controllable visual generation in the wild. arXiv preprint arXiv:2305.11147. find
  57. Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2):3.
  58. Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695. find
  59. Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2023. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22500–22510. runwayml.… find
  60. Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. 2022. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Syste…
  61. Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly. 2018. Assessing generative models via precision and recall. Advances in neural information processing systems, 31. find
  62. Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training gans. Advances in neural information processing systems, 29.
  63. Ayush Sarkar, Hanlin Mai, Amitabh Mahapatra, Svetlana Lazebnik, David Forsyth, and Anand Bhattad. 2023. Shadows don’t lie and lines can’t bend! generative models don’t know projective geometry...for now. ArXiv, abs/2311.17138. — [arXiv:2311.17138](https://arxiv.org/abs/2311.17138) find
  64. Shelly Sheynin, Adam Polyak, Uriel Singer, Yuval Kirstain, Amit Zohar, Oron Ashual, Devi Parikh, and Yaniv Taigman. 2023. Emu edit: Precise image editing via recognition and generation tasks. arXiv preprint arXiv:2311.10089. stability.ai. 2023. Stable diffusion xl. find
  65. Gemini Team, Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry, Lepikhin, Timothy Lillicrap, Jean baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, Ioannis Antonoglou, Rohan Anil, Sebastian Borgeaud, Andrew Dai, Katie Millican, Ethan Dyer, Mia Glaese, Thibau… — Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
  66. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. ArXiv, a…
  67. Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, Jiazheng Xu, Bin Xu, Juanzi Li, Yuxiao Dong, Ming Ding, and Jie Tang. 2023. Cogvlm: Visual expert for pretrained language models. ArXiv, abs/2311.03079.
  68. Chen Henry Wu and Fernando De la Torre. 2023. A latent space of stochastic diffusion models for zero-shot image editing and guidance. In ICCV. find
  69. Wenda Xu, Danqing Wang, Liangming Pan, Zhenqiao Song, Markus Freitag, William Yang Wang, and Lei Li. 2023. Instructscore: Towards explainable text generation evaluation with automatic feedback. ArXiv, abs/2305.14282.
  70. Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, and Lijuan Wang. 2023. The dawn of lmms: Preliminary explorations with gpt-4v(ision).
  71. Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, and Enhong Chen. 2023. A survey on multimodal large language models. ArXiv, abs/2306.13549.
  72. Kai Zhang, Lingbo Mo, Wenhu Chen, Huan Sun, and Yu Su. 2023a. Magicbrush: A manually annotated dataset for instruction-guided image editing. NeurIPS dataset and benchmark track. find
  73. Lvmin Zhang and Maneesh Agrawala. 2023. Adding conditional control to text-to-image diffusion models. arXiv preprint arXiv:2302.05543. find
  74. Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. 2018. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR. find
  75. Xinlu Zhang, Yujie Lu, Weizhi Wang, An Yan, Jun Yan, Lianke Qin, Heng Wang, Xifeng Yan, William Yang Wang, and Linda Ruth Petzold. 2023b. Gpt-4v(ision) as a generalist evaluator for vision-language tasks. ArXiv, abs/2311.01361. — [arXiv:2311.01361](https://arxiv.org/abs/2311.01361) find
  76. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena.

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/ku-2024-viescore-explainable-metrics-conditional-image/