Exploring Demonstration Ensembling for In-Context Learning

In-context learning (ICL) involves showing a language model (LM) examples of various input-output patterns for a task, while traditional approaches stimulate it further by concatenating demonstrations with test inputs.

This can skew evidence towards common patterns, like date formats or text structures. Prior work has explored improving example selection through clustering and graph-based search while increasing input diversity.

1. Demonstration ensembling can improve model performance

Traditional in-context learning involves prompting language models (LMs) with concatenated demonstrations followed by test input. This approach has proven successful for text classification and reasoning tasks, though its drawbacks include difficulty controlling the contribution of each demonstration to model prediction; additionally, often, there is insufficient sample size available to meet required demonstration counts.

Ensemble learning is another effective means of increasing model performance by combining predictions of multiple individual models into one more accurate forecast. There are various kinds of ensemble methods – product and weighted ensembles- two popular ones that can help improve classification, regression, and decision tree algorithms.

Ensembles can also be used to improve reinforcement learning (RL) performances. RL is a machine learning paradigm in which an agent learns a task by maximizing cumulative rewards; however, collecting significant rewards in complex tasks may take considerable time, and resources are needed for optimal rewards to accumulate quickly. To speed up learning processes, several methods have been created that incorporate prior knowledge and prior experience into the RL process more quickly, such as reward shaping or imitation learning techniques – one popular one being Reinforcement Learning from Demonstrations (RLfD), where expert policies guide an agent through tasks while making assumptions that shown state-action pairs represent samples from an optimal policy, which may lead to misguided or non-local action decisions being taken during actual charges.

2. Demonstration ensembling can improve model update

An ensemble model is a collection of models used together to generate predictions. It’s a popular approach to reduce bias and increase performance, such as stacking or blending techniques for modeling ensembles. Both methods use larger holdout datasets and prevent information leakage while combining smaller ones without issues.

Reinforcement Learning (RL) is an established machine learning paradigm with proven worth across various tasks such as robotic control, video games, and self-driving cars. However, due to the nature of reinforcement rewards being scattered or non-local in its application, RL may take an inordinately long time before reaching its desired goals.

Recent works have explored various approaches to speeding up RL using in-context learning. One such technique is demonstration ensembling, which involves prompting the RL model with multiple concatenated demonstrations and test inputs at once. Unfortunately, simple concatenation provides little control over each demonstration’s contribution to prediction, leading to overfitting and high variance of final output predictions.

3. Demonstration ensembling can improve model maintenance

Machine learning researchers have long known about in-context learning – an elusive phenomenon allowing large language models to perform new tasks without adjusting their parameters – with surprising ease. It has many practical uses, including step-by-step solutions to math word problems like “What is three times 4?” Unfortunately, the exact mechanism remains elusive, but popular theories hold that in-context learning detects some latent ability or knowledge acquired during pretraining that otherwise wouldn’t manifest in tasks after completion.

Recent papers have shed new light on in-context learning (ICL). Min et al. found that ICL examples’ distribution of inputs is incredibly crucial, while its format also affected performance – their empirical results suggested that random I-O mappings did not reduce it, suggesting LMs may already possess such knowledge from pretraining that ICL uses to leverage.

Similarly, Xie et al. examined the relationship between ICL inputs and input window size on performance. For numeric tasks such as addition, multiplication, and unit conversion, they discovered that performance was highly correlated with how often particular terms appeared in pretraining data – meaning an ICL model can accomplish ICL with fewer prompts simply by using how standard such words were in its training data.

4. Demonstration ensembling can improve model training

In-context learning enables language models to provide step-by-step solutions for various natural language problems, offering step-by-step solutions in real time. It’s an exciting area of machine learning with applications across numerous business functions; however, significant challenges such as interpretation ambiguity, transparency/explainability gaps, and domain-specific knowledge remain barriers to successful implementation.

Researchers have trained large language models using in-context learning techniques to perform well on tasks they weren’t prepared for, like solving math word problems with competitive few-shot performance. This remarkable feat allows these models to be utilized for tasks like translation and question-answering.

Language models contain hidden states with some linear models, which are updated using learning algorithms. When making predictions based on input data, this model searches for the forecast with the highest vote count as its output – usually chosen as its final output for that test instance.

The authors’ work expands this concept by presuming that the model is being trained on examples with both ground truth and random outputs, proving this method improves in-context learning by teaching its model to recognize which prompts will most likely produce accurate answers.