Scientists Just Found a Way to Skip AI Training Entirely. Here's How

Table of Links

Abstract and 1 Introduction

2 Related Work

3 Methods and 3.1 Models

3.2 Datasets

3.3 Evaluation Metrics

4 Results and 4.1 Increasing number of demonstrating examples

4.2 Impact of batching queries

4.3 Cost and latency analysis

5 Discussion

6 Conclusion and References

A. Prompts used for ICL experiments

B. Prompt selection

C. GPT4(V)-Turbo performance under many-shot ICL

D. Performance of many-shot ICL on medical QA tasks

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.

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