In-Context Learning vs. Weight Shifting: A Comparison of Approaches to Softmax Regression

In-context learning is a rapidly growing field that explores how machines can learn from limited data in specific contexts, such as language models generating text based on prompts or images classified by their captions.

Shuai Li et al. analyze the connection between in-context learning and weight shifting for softmax regression, demonstrating that these concepts are closely related. They show how weight shifting can be used to improve the performance of in-context learning models.

Bingbin Liu et al. explore transformers’ ability to learn shortcuts or heuristics to automata, which are simple models that can recognize and generate language. They show how these shortcuts enable transformers to generalize better and improve their performance in various tasks.

Yao Lu et al. investigate the importance of prompts in in-context learning and introduce the concept of "fantastically or-dered prompts," which are prompts that are both fantastic (i.e., interesting) and well-ordered (i.g., containing a clear topic and supporting details). They demonstrate how these prompts can improve the performance of in-context learning models.

Peter J Liu et al. propose a method for generating articles on Wikipedia by summarizing long sequences of text, rather than relying solely on statistical methods. They show how this approach can improve the quality and coherence of generated articles.

Ashish Vaswani et al. discuss the challenge of few-shot prompt order sensitivity in in-context learning, where models are sensitive to the order of prompts even when the content is the same. They propose a solution based on using attention mechanisms to focus on relevant parts of the input.

Johannes von Oswald et al. investigate how transformer models learn in-context by gradient descent, showing that this process involves learning to predict contextualized representations of words and phrases. They demonstrate how this learning mechanism can be used to improve the performance of transformer models in various natural language processing tasks.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model:
Ben Wang and Aran Komatsuzaki introduce GPT-J-6B, a 6 billion parameter autoregressive language model that uses in-context learning to generate text. They demonstrate how this model can be used for various natural language processing tasks, such as language translation and text summarization.
Quantifying and Extrapolating the Capabilities of Language Models:
Aarohi Srivastava et al. propose a framework for quantifying and extrapolating the capabilities of language models based on their performance on various tasks. They demonstrate how this framework can be used to compare the performance of different language models and identify areas where they can be improved.

In-context learning is a rapidly growing field that explores how machines can learn from limited data in specific contexts. This survey provides an overview of recent advances in in-context learning, including the connection between in-context learning and weight shifting for softmax regression, transformers’ ability to learn shortcuts or heuristics to automata, the importance of prompts in in-context learning, and the use of attention mechanisms to overcome few-shot prompt order sensitivity. These advances have demonstrated the potential of in-context learning to improve the performance of various natural language processing tasks and expand the capabilities of language models.