Reinforcement Learning for Optimal Selection of In-Context Examples to Improve Language Model Performance

Grunnleggende konsepter

RetICL, a learnable method for sequentially selecting in-context examples to optimize language model performance on downstream tasks, outperforms heuristic and learnable baselines by modeling the dependencies between examples and the order in which they are provided.

Sammendrag

The authors propose RetICL, a reinforcement learning-based method for sequentially selecting in-context examples to optimize language model performance on downstream tasks. The key insights are:

In-context learning (ICL) with large language models often relies on carefully selected examples, but existing methods treat example selection independently, ignoring dependencies between examples and their order.

RetICL frames the problem of sequential example selection as a Markov decision process and trains an example retriever model using reinforcement learning. The retriever model constructs a latent representation of the current state (problem and previously selected examples) and uses a bilinear transformation to score and select the next example.

RetICL introduces a novel confidence-based reward function that uses the perplexity of the generated solution to guide the training of the retriever model.

Experiments on math word problem solving and scientific question answering tasks show that RetICL consistently outperforms or matches heuristic and learnable baselines. Qualitative analysis reveals that RetICL implicitly learns representations of problem-solving strategies.

RetICL is effective even in low-resource settings with limited available examples. However, the method can be computationally expensive to train, requiring a large number of language model inferences.

Overall, RetICL demonstrates the benefits of modeling the sequential and interdependent nature of in-context example selection to improve language model performance on downstream tasks.

Statistikk

"Recent developments in large pre-trained language models have enabled unprecedented performance on a variety of downstream tasks."
"Achieving best performance with these models often leverages in-context learning, where a model performs a (possibly new) task given one or more examples."
"The choice of examples can have a large impact on task performance and that finding an optimal set of examples is non-trivial."

Sitater

"While there are many existing methods for selecting in-context examples, they generally score examples independently, ignoring the dependency between them and the order in which they are provided to the model."
"We frame the problem of sequential example selection as a Markov decision process and train an example retriever using reinforcement learning."
"We evaluate RetICL on math word problem solving and scientific question answering tasks and show that it consistently outperforms or matches heuristic and learnable baselines."

Viktige innsikter hentet fra

RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning

by Alexander Sc... klokken arxiv.org 04-17-2024

https://arxiv.org/pdf/2305.14502.pdf

Dypere Spørsmål

How can the computational cost of training RetICL be reduced while maintaining its performance benefits?

To reduce the computational cost of training RetICL while preserving its performance benefits, several strategies can be implemented:

Batch Training: Instead of training on individual examples, batch training can be utilized to update the model's parameters using multiple examples simultaneously. This can help reduce the number of forward and backward passes required during training, thereby decreasing the overall computational cost.

Model Pruning: Implementing model pruning techniques can help reduce the number of parameters in the retriever model, leading to faster training times and lower computational costs. By removing unnecessary parameters, the model can become more efficient without sacrificing performance.

Parallel Processing: Utilizing parallel processing techniques, such as distributed training across multiple GPUs or using specialized hardware like TPUs, can significantly speed up the training process and reduce computational costs. This approach allows for faster iterations and quicker convergence of the model.

Transfer Learning: Leveraging pre-trained models and fine-tuning them on the specific task can reduce the training time and computational resources required. By starting with a pre-trained model that has already learned general language patterns, the model can adapt more quickly to the new task.

Optimized Hyperparameters: Fine-tuning hyperparameters such as learning rate, batch size, and optimizer settings can lead to faster convergence and more efficient training. By optimizing these parameters, the model can achieve the desired performance with fewer computational resources.

How could RetICL be extended to handle dynamic selection of the number of in-context examples, rather than a fixed number, to further optimize language model performance?

To enable dynamic selection of the number of in-context examples in RetICL, the following modifications can be made:

Dynamic Policy: Implement a dynamic policy that can adapt the number of examples selected based on the complexity of the current problem. The policy can learn to determine the optimal number of examples required for each problem, adjusting the selection process accordingly.

Reinforcement Learning: Train the retriever model using reinforcement learning to learn the optimal number of examples for each problem dynamically. By incorporating a reward signal that incentivizes selecting the right number of examples, the model can learn to adjust the selection process based on the problem at hand.

Variable-Length Sequences: Modify the model architecture to handle variable-length sequences of examples. This would involve designing the model to accept a varying number of examples as input and dynamically adjust the processing for each problem.

Attention Mechanisms: Utilize attention mechanisms to focus on the most relevant examples for each problem. By dynamically attending to different examples based on their importance, the model can effectively select the optimal number of examples for each task.

Sequential Decision Making: Frame the problem of selecting the number of examples as a sequential decision-making process, where the model iteratively decides whether to include additional examples based on the current context. This approach allows the model to adapt its decision dynamically throughout the selection process.

By incorporating these strategies, RetICL can be extended to handle dynamic selection of the number of in-context examples, leading to further optimization of language model performance based on the specific requirements of each task.