@inproceedings{li-etal-2024-vlfeedback,
title = "{VLF}eedback: A Large-Scale {AI} Feedback Dataset for Large Vision-Language Models Alignment",
author = "Li, Lei and
Xie, Zhihui and
Li, Mukai and
Chen, Shunian and
Wang, Peiyi and
Chen, Liang and
Yang, Yazheng and
Wang, Benyou and
Kong, Lingpeng and
Liu, Qi",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.358",
doi = "10.18653/v1/2024.emnlp-main.358",
pages = "6227--6246",
abstract = "As large vision-language models (LVLMs) evolve rapidly, the demand for high-quality and diverse data to align these models becomes increasingly crucial. However, the creation of such data with human supervision proves costly and time-intensive. In this paper, we investigate the efficacy of AI feedback to scale supervision for aligning LVLMs. We introduce VLFeedback, the first large-scale vision-language feedback dataset, comprising over 82K multi-modal instructions and comprehensive rationales generated by off-the-shelf models without human annotations. To evaluate the effectiveness of AI feedback for vision-language alignment, we train Silkie, an LVLM fine-tuned via direct preference optimization on VLFeedback. Silkie showcases exceptional performance regarding helpfulness, visual faithfulness, and safety metrics. It outperforms its base model by 6.9{\%} and 9.5{\%} in perception and cognition tasks, reduces hallucination issues on MMHal-Bench, and exhibits enhanced resilience against red-teaming attacks. Furthermore, our analysis underscores the advantage of AI feedback, particularly in fostering preference diversity to deliver more comprehensive improvements. Our dataset, training code and models are available at \url{https://vlf-silkie.github.io}.",
}
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<abstract>As large vision-language models (LVLMs) evolve rapidly, the demand for high-quality and diverse data to align these models becomes increasingly crucial. However, the creation of such data with human supervision proves costly and time-intensive. In this paper, we investigate the efficacy of AI feedback to scale supervision for aligning LVLMs. We introduce VLFeedback, the first large-scale vision-language feedback dataset, comprising over 82K multi-modal instructions and comprehensive rationales generated by off-the-shelf models without human annotations. To evaluate the effectiveness of AI feedback for vision-language alignment, we train Silkie, an LVLM fine-tuned via direct preference optimization on VLFeedback. Silkie showcases exceptional performance regarding helpfulness, visual faithfulness, and safety metrics. It outperforms its base model by 6.9% and 9.5% in perception and cognition tasks, reduces hallucination issues on MMHal-Bench, and exhibits enhanced resilience against red-teaming attacks. Furthermore, our analysis underscores the advantage of AI feedback, particularly in fostering preference diversity to deliver more comprehensive improvements. Our dataset, training code and models are available at https://vlf-silkie.github.io.</abstract>
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%0 Conference Proceedings
%T VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment
%A Li, Lei
%A Xie, Zhihui
%A Li, Mukai
%A Chen, Shunian
%A Wang, Peiyi
%A Chen, Liang
%A Yang, Yazheng
%A Wang, Benyou
%A Kong, Lingpeng
%A Liu, Qi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F li-etal-2024-vlfeedback
%X As large vision-language models (LVLMs) evolve rapidly, the demand for high-quality and diverse data to align these models becomes increasingly crucial. However, the creation of such data with human supervision proves costly and time-intensive. In this paper, we investigate the efficacy of AI feedback to scale supervision for aligning LVLMs. We introduce VLFeedback, the first large-scale vision-language feedback dataset, comprising over 82K multi-modal instructions and comprehensive rationales generated by off-the-shelf models without human annotations. To evaluate the effectiveness of AI feedback for vision-language alignment, we train Silkie, an LVLM fine-tuned via direct preference optimization on VLFeedback. Silkie showcases exceptional performance regarding helpfulness, visual faithfulness, and safety metrics. It outperforms its base model by 6.9% and 9.5% in perception and cognition tasks, reduces hallucination issues on MMHal-Bench, and exhibits enhanced resilience against red-teaming attacks. Furthermore, our analysis underscores the advantage of AI feedback, particularly in fostering preference diversity to deliver more comprehensive improvements. Our dataset, training code and models are available at https://vlf-silkie.github.io.
%R 10.18653/v1/2024.emnlp-main.358
%U https://aclanthology.org/2024.emnlp-main.358
%U https://doi.org/10.18653/v1/2024.emnlp-main.358
%P 6227-6246
Markdown (Informal)
[VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment](https://aclanthology.org/2024.emnlp-main.358) (Li et al., EMNLP 2024)
ACL
- Lei Li, Zhihui Xie, Mukai Li, Shunian Chen, Peiyi Wang, Liang Chen, Yazheng Yang, Benyou Wang, Lingpeng Kong, and Qi Liu. 2024. VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6227–6246, Miami, Florida, USA. Association for Computational Linguistics.