In-Context Learning Capabilities of Multi-Layer Perceptrons MLPs: A Comparative Study with Transformers

Practical Solutions with AI In-Context Learning Capabilities In recent years, there have been significant advancements in neural language models, particularly Large Language Models (LLMs) enabled by the Transformer architecture and increased scale. These models excel in generating grammatical text, answering questions, summarizing content, creating imaginative outputs, and solving complex puzzles. Value of In-Context Learning (ICL) and Practical Solutions One key capability is in-context learning (ICL), where the model uses new task examples presented during inference to respond accurately without weight updates. ICL is typically associated with Transformers and their attention-based mechanisms. ICL has been demonstrated for linear regression tasks with Transformers, which can generalize to new input/label pairs in-context. Transformers achieve this by potentially implementing gradient descent or replicating least-squares regression. Transformers interpolate between in-weight learning (IWL) and ICL, with diverse datasets enhancing ICL capabilities. A study has shown that multi-layer perceptrons (MLPs) can effectively learn in-context and perform competitively with Transformers on ICL tasks within the same compute budget. Particularly, MLPs outperform Transformers in relational reasoning ICL tasks, challenging the belief that ICL is unique to Transformers. Comparative Study of MLPs and Transformers The study investigates MLPs’ behavior in ICL through two tasks: in-context regression and in-context classification. MLPs and Transformers were compared on these tasks, with both architectures achieving near-optimal mean squared error (MSE) with sufficient computing. As data diversity increased, all models transitioned from IWL to ICL, with Transformers making the transition more quickly. In in-context classification, MLPs performed comparably to Transformers, maintaining relatively flat loss across context lengths and transitioning from IWL to ICL with increased data diversity. AI Redefining Work Processes To evolve your company with AI, stay competitive, and use In-Context Learning Capabilities of Multi-Layer Perceptrons MLPs, you can redefine your way of work through AI solutions. To get started, you can: Identify Automation Opportunities Define KPIs Select an AI Solution Implement Gradually Spotlight on a Practical AI Solution Consider the AI Sales Bot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. This solution can redefine your sales processes and customer engagement. For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com or stay tuned on our Telegram Channel or Twitter. Discover more about AI solutions at itinai.com/aisalesbot. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom