Master the science of understanding how transformer-based language models actually work internally, from attention patterns to emergent behaviors and circuit-level analysis.
Chain-of-Thought Mechanisms: Understanding how models implement step-by-step reasoning, including the identification of internal components responsible for maintaining and manipulating working memory.
Logical Inference Circuits: Analyzing how models perform logical operations like modus ponens, contraposition, and other fundamental reasoning patterns at the circuit level.
Abstract Reasoning Pathways: Identifying the neural mechanisms underlying analogical reasoning, pattern recognition, and other forms of abstract thinking.
Factual Knowledge Storage: Understanding how models store and retrieve factual information, including the distributed nature of knowledge representation across parameters.
Conceptual Hierarchy Formation: Analyzing how models learn hierarchical concept structures and how these hierarchies influence reasoning and inference.
Knowledge Composition: Understanding how models combine disparate pieces of knowledge to answer novel questions or solve unfamiliar problems.
Syntactic Processing Circuits: Identifying the specific neural pathways responsible for parsing syntax, handling grammatical structures, and maintaining linguistic coherence.
Semantic Composition: Understanding how models build meaning from component parts, including compositional semantics and context-dependent interpretation.
Pragmatic Inference: Analyzing how models handle implied meaning, context-dependent interpretation, and other aspects of pragmatic language understanding.