Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Methods and 3.1 Models 3 Methods and 3.1 Models 3.2 Datasets 3.2 Datasets 3.3 Evaluation Metrics 3.3 Evaluation Metrics 4 Results and 4.1 Increasing number of demonstrating examples 4 Results and 4.1 Increasing number of demonstrating examples 4.2 Impact of batching queries 4.2 Impact of batching queries 4.3 Cost and latency analysis 4.3 Cost and latency analysis 5 Discussion 5 Discussion 6 Conclusion and References 6 Conclusion and References A. Prompts used for ICL experiments A. Prompts used for ICL experiments B. Prompt selection B. Prompt selection C. GPT4(V)-Turbo performance under many-shot ICL C. GPT4(V)-Turbo performance under many-shot ICL D. Performance of many-shot ICL on medical QA tasks D. Performance of many-shot ICL on medical QA tasks Acknowledgments and Disclosure of Funding Acknowledgments and Disclosure of Funding 5 Discussion In this study, we evaluate many-shot ICL of state-of-the-art multimodal foundation models across 10 datasets and find consistent performance improvements across most of the datasets. Batching queries with many-shot ICL further exhibits substantially reduced per-example latency and inference costs without compromising performance. Our findings suggest that these multimodal foundation models have the capability of performing ICL with large numbers of demonstrating examples, which may have significant implications on their practical use. For example, it was previously impossible to adapt these large, private models to new tasks and domains, but many-shot ICL would enable users to leverage demonstrating examples to adapt the models. One significant advantage of many-shot ICL is its ability to get quick results even on the same day of model release, and that’s why we can finish our evaluation using GPT-4o within days. Furthermore, fine-tuning open-source models is the standard practice when practitioners have access to moderately sized datasets, but many-shot ICL may remove the need for fine-tuning, making it much easier to develop customized approaches. We note that it remains to be seen how traditional fine-tuning of these models compares to many-shot ICL with foundation models in terms of absolute performance and data efficiency, so future work should explore this. In addition, it is important to study general issues which plague those foundation models, such as hallucinations and biases, under the context of many-shot ICL and batching queries. For example, it would be interesting to explore if carefully curated and large sets of demonstrating examples can reduce biases across different sub-groups. We leave this to future work. Our study has limitations. First, we only explore performance under many-shot ICL on image classification tasks and with private foundation models. We believe these are the most practically relevant and common multimodal settings, but it is worthwhile for future work to explore potential benefits from many-shot ICL on other tasks and with upcoming open-source multimodal foundation models like LLaMA-3 [30]. Second, even after recent developments to increase context size, the size prohibits many-shot ICL from being used on datasets with a large number (several hundred or more) of classes. We anticipate that context window sizes will continue to increase in size over time which will mitigate this issue. Third, the datasets which were used to train these private models have not been disclosed, so it is difficult to tell whether the models have been trained on the datasets we selected. We argue that zero-shot performance across the datasets is far from perfect which provides evidence that the datasets have not been used for training, but we cannot determine that with certainty. 6 Conclusion In summary, we show that multimodal foundation models are capable of many-shot ICL. We believe that these results pave a promising path forward to improve the adaptability and accessibility of large multimodal foundation models. References [1] 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, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners, 2020. 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In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3606–3613, 2014. [28] Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren, Ding Xue, et al. Drugood: Out-of-distribution (ood) dataset curator and benchmark for ai-aided drug discovery–a focus on affinity prediction problems with noise annotations. arXiv preprint arXiv:2201.09637, 2022. [29] Wei-Lin Chen, Cheng-Kuang Wu, and Hsin-Hsi Chen. Self-icl: Zero-shot in-context learning with self-generated demonstrations. arXiv preprint arXiv:2305.15035, 2023. [30] Introducing meta llama 3: The most capable openly available llm to date. URL https: //ai.meta.com/blog/meta-llama-3/. Authors:
(1) Yixing Jiang, Stanford University (jiang6ord@cs.stanford.edu);
(2) Jeremy Irvin, Stanford University (jirvin16@cs.stanford.edu);
(3) Ji Hun Wang, Stanford University (mahmedch@stanford.edu);
(4) Muhammad Ahmed Chaudhry, Stanford University (mahmedch@stanford.edu);
(5) Jonathan H. Chen, Stanford University (jonc101@stanford.edu);
(6) Andrew Y. Ng, Stanford University (ang@cs.stanford.edu). Authors: Authors: (1) Yixing Jiang, Stanford University (jiang6ord@cs.stanford.edu); (2) Jeremy Irvin, Stanford University (jirvin16@cs.stanford.edu); (3) Ji Hun Wang, Stanford University (mahmedch@stanford.edu); (4) Muhammad Ahmed Chaudhry, Stanford University (mahmedch@stanford.edu); (5) Jonathan H. Chen, Stanford University (jonc101@stanford.edu); (6) Andrew Y. Ng, Stanford University (ang@cs.stanford.edu). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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