Benchmarking Many-Shot In-Context Learning with Closed and Open-Weights Multimodal Foundation Models

المفاهيم الأساسية

Many-shot in-context learning significantly improves the performance of closed-weights multimodal foundation models, particularly Gemini 1.5 Pro, across diverse vision tasks, while open-weights models do not yet exhibit this capability.

الملخص

This research paper investigates the impact of many-shot in-context learning (ICL) on the performance of large multimodal models (LMMs). The authors benchmark the performance of three closed-weights LMMs (GPT-4o, GPT4(V)-Turbo, and Gemini 1.5 Pro) and two open-weights LMMs (Llama3.2-11B-Vision and InternLM-XComposer-2.5) on 14 datasets spanning various vision domains and tasks.

Research Objective

The study aims to determine if providing LMMs with a large number of demonstrating examples during inference, without updating model parameters, can improve their performance on various vision tasks.

Methodology

The researchers evaluate the models' performance using standard metrics like accuracy, macro-averaged F1, and mean Intersection over Union (IoU) for different tasks. They experiment with increasing numbers of demonstrating examples, up to approximately 2,000, to assess the impact of many-shot ICL. Additionally, they explore the effects of batching multiple queries in a single prompt to reduce inference cost and latency.

Key Findings

• Closed-weights LMMs, especially Gemini 1.5 Pro, demonstrate substantial performance improvements with many-shot ICL compared to few-shot and zero-shot settings.
• Gemini 1.5 Pro exhibits a log-linear performance improvement with increasing demonstration examples on most datasets.
• Open-weights LMMs do not benefit from many-shot ICL, highlighting a performance gap compared to closed-weights models.
• Batching queries in many-shot ICL significantly reduces per-example latency and inference cost without compromising performance.
• Batching queries even improves zero-shot performance, potentially due to domain calibration, class calibration, and self-generated demonstrations.

Main Conclusions

Many-shot ICL is a promising approach to enhance the adaptability and performance of closed-weights LMMs on new tasks and domains without further training. The authors suggest that future research should focus on bridging the performance gap between open and closed-weights models in many-shot ICL.

Significance

This research significantly contributes to the field of multimodal learning by demonstrating the potential of many-shot ICL for adapting large models to new tasks without fine-tuning. This capability has significant practical implications, making LMMs more versatile and accessible for various applications.

Limitations and Future Research

The study is limited by the context window size of current LMMs, restricting the number of demonstrating examples usable for tasks with many classes. Future research could explore techniques to overcome this limitation. Additionally, investigating the generalizability of these findings to other multimodal tasks and comparing many-shot ICL with fine-tuning in terms of performance and data efficiency are promising research avenues.

الإحصائيات

Gemini 1.5 Pro performance improves log-linearly up to ~1,000 examples on 8 out of 14 datasets.
Gemini 1.5 Pro shows a performance increase of +23% accuracy on HAM10000 compared to zero-shot and +16% compared to 7 examples.
Gemini 1.5 Pro shows a performance increase of +29% accuracy on FIVES compared to zero-shot and +27% compared to 20 examples.
Gemini 1.5 Pro shows a performance increase of +38% accuracy on EuroSAT compared to zero-shot and +31% compared to 10 examples.
Both Gemini 1.5 Pro and GPT-4o achieve an average improvement of +17% accuracy at the optimal demo set size.
Batching up to 50 queries reduces per-example latency by nearly 35x and cost by 10x for HAM10000 with many-shot ICL.
Batching up to 50 queries reduces per-example latency by 20x and cost by 45x for TerraIncognita with many-shot ICL.

اقتباسات

"We show that providing close-weights multimodal foundation models with many demonstrating examples leads to substantial performance improvements compared to providing only a few demonstrating examples."
"We find open-weights multimodal foundation models like Llama 3.2-Vision and InternLM-XComposer2.5 do not benefit from the demonstrating examples, highlighting a significant gap and an important direction for the open-weights community."
"We demonstrate that batching multiple queries into a single request can achieve similar or better performance than single query requests in a many-shot setting, while enabling substantially lower per-example latency and much cheaper per-example inference cost."

الرؤى الأساسية المستخلصة من

Many-Shot In-Context Learning in Multimodal Foundation Models

by Yixing Jiang... في arxiv.org 10-08-2024

https://arxiv.org/pdf/2405.09798.pdf

استفسارات أعمق

How does the quality and diversity of demonstrating examples affect the performance of many-shot ICL in multimodal models?

The quality and diversity of demonstrating examples are crucial for the effectiveness of many-shot in-context learning (ICL) in multimodal models. Here's a breakdown of how these factors impact performance:
Quality:

Relevance: Demonstrating examples must be pertinent to the target task and domain. Irrelevant examples can confuse the model and hinder learning.
Accuracy: The labels or answers associated with the examples should be accurate. Incorrect information will propagate through the learning process, leading to poor generalization.
Clarity: Images should be clear and representative of the concepts they depict. Similarly, text descriptions in the examples should be well-formed and unambiguous.
Diversity:

Class Representation: For classification tasks, demonstrating examples should adequately represent the distribution of classes in the target dataset. An imbalance can bias the model towards over-represented classes.
Intra-class Variation: Within each class, examples should capture the inherent variability. This helps the model learn robust features and generalize to unseen instances.
Domain Coverage: If the target domain is diverse, demonstrating examples should reflect this diversity. This is particularly important for tasks like visual question answering (VQA) and object localization, where the model needs to understand the relationship between visual and textual information across different contexts.
Key Considerations:

Data Augmentation: Techniques like image cropping, rotation, and color adjustments can be used to artificially increase the diversity of demonstrating examples, improving model robustness.
Example Selection Strategies: Research into optimal example selection strategies is ongoing. Techniques like active learning and core-set selection could be leveraged to identify the most informative and diverse examples for ICL.
In essence, high-quality, diverse demonstrating examples provide a rich training signal for multimodal models during ICL. This allows them to better understand the task, learn relevant features, and generalize effectively to new inputs.

Could techniques like prompt engineering or fine-tuning further enhance the performance of open-weights models in many-shot ICL settings?

Yes, techniques like prompt engineering and fine-tuning hold significant potential for enhancing the performance of open-weight models in many-shot ICL settings.
Prompt Engineering:

Optimized Prompt Structure: Carefully designing the structure and wording of prompts can significantly impact performance. This includes aspects like:

Clear Instructions: Providing explicit instructions to the model about the task and desired output format.
Contextualization: Framing the task within a relevant context to guide the model's understanding.
Example Formatting: Experimenting with different ways to present demonstrating examples within the prompt (e.g., image-text ordering, use of delimiters).

Prompt Augmentation: Techniques like:

Dynamic Prompting: Adapting prompts based on the input query or context.
Knowledge Injection: Incorporating external knowledge or constraints into the prompt to guide the model.
Fine-tuning:

Adapting to ICL: While traditional fine-tuning updates model parameters on a downstream task, research is exploring methods to fine-tune models specifically for improved ICL capabilities. This could involve:

Meta-learning: Training the model on a variety of tasks to improve its ability to learn from few examples.
Prompt Optimization: Fine-tuning the model to better understand and utilize information presented in the prompt.

Domain Adaptation: Fine-tuning open-weight models on data from the target domain can bridge the performance gap with closed-weight models.
Key Considerations:

Computational Resources: Fine-tuning large multimodal models can be computationally expensive.
Data Requirements: Fine-tuning generally requires more data than ICL, although techniques like parameter-efficient fine-tuning can mitigate this.
By strategically combining prompt engineering and fine-tuning, it's likely that the performance of open-weight models in many-shot ICL settings can be significantly improved, making them more accessible and effective for a wider range of applications.

What are the ethical implications of using many-shot ICL, particularly concerning potential biases amplified through the selection of demonstrating examples?

The use of many-shot ICL, while promising, raises important ethical considerations, particularly regarding the potential for amplifying biases present in the data used for demonstrating examples.
Here are key areas of concern:

Representation Bias: If the demonstrating examples underrepresent certain groups or portray them in stereotypical ways, the model may learn and perpetuate these biases. For instance, a model trained on examples primarily featuring light-skinned individuals for a skin cancer detection task might perform poorly on images of darker skin tones.
Association Bias: The selection of demonstrating examples can implicitly encode harmful associations. For example, if a model is primarily shown images of women in domestic settings and men in professional settings for a task related to occupation prediction, it might develop biased associations between gender and career choices.
Confirmation Bias: Many-shot ICL could be used to intentionally reinforce pre-existing biases. By carefully selecting demonstrating examples that align with a particular viewpoint, users could potentially manipulate the model's outputs.
Mitigating Bias:

Diverse and Representative Datasets: Using demonstrating examples from diverse and representative datasets is crucial. This requires careful data collection and annotation practices that consider potential biases.
Bias Detection and Mitigation Techniques: Employing techniques to detect and mitigate bias in both the demonstrating examples and the model's outputs is essential. This includes methods for:

Data Auditing: Analyzing the demonstrating examples for potential biases.
Bias Mitigation during Training: Developing training procedures that encourage fairness and reduce bias amplification.
Output Evaluation: Regularly evaluating the model's outputs for bias across different subgroups.

Transparency and Accountability: Clearly communicating the limitations of many-shot ICL and the potential for bias is crucial. Users should be aware of the data used to generate demonstrating examples and the steps taken to mitigate bias.
Ethical Considerations:

Responsible Use: It's crucial to use many-shot ICL responsibly and ethically, considering its potential impact on individuals and society.
Ongoing Research: Continued research is needed to develop robust methods for detecting, mitigating, and preventing bias in many-shot ICL systems.
Addressing these ethical challenges is paramount to ensure that the development and deployment of many-shot ICL technologies are fair, equitable, and beneficial to all.