Scaling In-Context Learning: Significant Performance Gains from Few-Shot to Many-Shot Regimes

Conceptos Básicos

Scaling the number of in-context examples (shots) leads to significant performance gains across a wide variety of generative and discriminative tasks for large language models.

Resumen

The paper investigates how scaling the number of in-context examples (shots) affects the performance of large language models (LLMs) across diverse downstream tasks. Key findings:

Transitioning from few-shot to many-shot learning regime leads to consistent performance improvements across tasks like machine translation, summarization, planning, code verification, and problem-solving.

For complex reasoning tasks, the authors introduce "Reinforced ICL" and "Unsupervised ICL" to mitigate the need for high-quality human-generated outputs in the prompt. These approaches can outperform few-shot ICL with human-generated rationales.

Many-shot ICL can overcome pre-training biases and learn high-dimensional numerical prediction tasks where few-shot ICL struggles. However, the order of examples in the prompt can significantly influence performance even in the many-shot setting.

The widely-used next-token prediction loss may not reliably predict downstream performance, especially on problem-solving and reasoning tasks.

Estadísticas

Many-shot ICL achieves 4.5% and 1.5% higher BLEU scores compared to 1-shot ICL on English-Kurdish and English-Tamil machine translation, respectively.
On the XSum summarization task, many-shot ICL reaches performance close to specialized summarization models fine-tuned on the task.
On the Logistics planning domain, the success rate improves from 0% to 40% with 800 shots.
For the code verifier task, the best-of-4 accuracy reaches 82% with 128 shots, bridging the gap between pass@1 and pass@4 accuracy.

Citas

"Many-shot learning holds significant promise, but it can be constrained by the need for high-quality, human-generated outputs."
"Reinforced ICL involves replacing human-written rationales with model-generated ones, filtered via answer correctness, for in-context learning."
"Unsupervised ICL prompts the model only with problems instead of problem-solution pairs."

Ideas clave extraídas de

Many-Shot In-Context Learning

by Rishabh Agar... a las arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11018.pdf

Consultas más profundas

How can we further improve the quality and reliability of model-generated rationales for many-shot ICL on complex reasoning tasks?

In order to enhance the quality and reliability of model-generated rationales for many-shot In-Context Learning (ICL) on complex reasoning tasks, several strategies can be implemented:

Diverse Training Data: Providing the model with a diverse range of training examples can help it generate more accurate and reliable rationales. By exposing the model to a wide variety of scenarios and problem types, it can learn to generate rationales that are more robust and applicable across different contexts.

Fine-tuning on Generated Rationales: After generating model-generated rationales, fine-tuning the model on these generated examples can help improve the quality of the rationales. This iterative process of generating rationales, evaluating them, and fine-tuning the model can lead to more accurate and reliable outputs.

Human Oversight and Validation: Incorporating human oversight and validation in the generation of model-generated rationales can help ensure their quality and reliability. Human annotators can review the generated rationales, provide feedback, and correct any inaccuracies, helping the model learn and improve over time.

Adversarial Training: Introducing adversarial examples during training can help the model learn to generate more robust and accurate rationales. By exposing the model to challenging scenarios and edge cases, it can improve its ability to generate reliable rationales in complex reasoning tasks.

Regular Evaluation and Feedback Loop: Continuously evaluating the quality of the model-generated rationales on a validation set and incorporating feedback into the training process can help improve their reliability over time. This feedback loop ensures that the model adapts and learns from its mistakes, leading to more accurate outputs.

By implementing these strategies and techniques, we can further enhance the quality and reliability of model-generated rationales for many-shot ICL on complex reasoning tasks.

What are the potential downsides or limitations of relying too heavily on many-shot ICL instead of task-specific fine-tuning?

While many-shot In-Context Learning (ICL) offers significant advantages in terms of versatility, adaptability, and performance across a wide range of tasks, there are potential downsides and limitations to relying too heavily on this approach instead of task-specific fine-tuning:

Overfitting to Training Data: Depending too heavily on many-shot ICL without task-specific fine-tuning can lead to overfitting to the training data. The model may become too specialized in certain tasks and struggle to generalize to new or unseen scenarios.

Lack of Task-Specific Optimization: Task-specific fine-tuning allows for targeted optimization of the model for a particular task, leading to better performance and efficiency. Relying solely on many-shot ICL may not provide the same level of task-specific optimization.

Limited Adaptability: Task-specific fine-tuning enables the model to quickly adapt to new tasks or domains by updating specific parameters. Many-shot ICL, on the other hand, may require a large number of examples to adapt effectively, limiting its adaptability in real-time or dynamic environments.

Human Annotation Dependency: Many-shot ICL often relies on human-generated examples for training, which can be time-consuming and resource-intensive. Task-specific fine-tuning may offer a more efficient and cost-effective alternative in some cases.

Biases and Generalization: Task-specific fine-tuning allows for the correction of biases and the enhancement of generalization capabilities for specific tasks. Relying solely on many-shot ICL may not address these issues effectively, leading to biased or less generalized models.

Overall, while many-shot ICL is a powerful tool for learning from a large number of examples, it is essential to balance its use with task-specific fine-tuning to ensure optimal performance and adaptability across diverse tasks and domains.

How might the insights from this work on scaling in-context learning apply to other domains beyond language models, such as reinforcement learning or multi-agent systems?

The insights gained from scaling in-context learning in language models can be applied to other domains beyond language models, such as reinforcement learning and multi-agent systems, in the following ways:

Sample Efficiency: Just as many-shot ICL improves sample efficiency in language models, similar techniques can be applied to reinforcement learning to enhance learning from limited data. By providing agents with a large number of in-context examples, they can learn more efficiently and effectively.

Generalization: Many-shot ICL has shown to improve generalization capabilities in language models. This concept can be extended to reinforcement learning and multi-agent systems to enhance the ability of agents to generalize across different environments and scenarios.

Bias Mitigation: Insights from overcoming pre-training biases in language models through many-shot ICL can be valuable in reinforcement learning and multi-agent systems. By exposing agents to diverse examples and scenarios, biases can be mitigated, leading to more fair and unbiased decision-making.

Adaptability: The adaptability of many-shot ICL to new tasks and domains can be leveraged in reinforcement learning and multi-agent systems. Agents can quickly adapt to changing environments and tasks by learning from a large number of in-context examples, improving their overall performance and flexibility.

Human-Machine Collaboration: The use of model-generated rationales and unsupervised ICL can facilitate human-machine collaboration in reinforcement learning and multi-agent systems. By incorporating human feedback and validation, agents can learn more effectively and collaborate with humans in complex decision-making processes.

By applying the principles and techniques of scaling in-context learning from language models to other domains like reinforcement learning and multi-agent systems, we can enhance the efficiency, generalization, adaptability, and fairness of AI systems across a wide range of applications.