Modeling Language with Thought Gestalts
Analysis
This paper introduces the Thought Gestalt (TG) model, a recurrent Transformer that models language at two levels: tokens and sentence-level 'thought' states. It addresses limitations of standard Transformer language models, such as brittleness in relational understanding and data inefficiency, by drawing inspiration from cognitive science. The TG model aims to create more globally consistent representations, leading to improved performance and efficiency.
Key Takeaways
- •Proposes the Thought Gestalt (TG) model, a novel architecture for language modeling.
- •TG models language at token and sentence levels, inspired by cognitive science.
- •Demonstrates improved efficiency and reduced errors on relational tasks compared to GPT-2.
- •Addresses limitations of standard Transformer models in terms of relational understanding and data efficiency.
Reference / Citation
View Original"TG consistently improves efficiency over matched GPT-2 runs, among other baselines, with scaling fits indicating GPT-2 requires ~5-8% more data and ~33-42% more parameters to match TG's loss."