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Found 3 papers, 1 papers with code
Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion
The framework includes a representation that captures the general \textit{syntax} of structural recursion, coupled with two different frameworks for understanding their \textit{semantics} -- one that is more natural from a programming languages perspective and one that helps bridge that perspective with a mechanistic understanding of the underlying transformer architecture.
Linear Representations of Sentiment in Large Language Models
Sentiment is a pervasive feature in natural language text, yet it is an open question how sentiment is represented within Large Language Models (LLMs).
Can Transformers Learn to Solve Problems Recursively?
Neural networks have in recent years shown promise for helping software engineers write programs and even formally verify them.
