Understanding the Internal Processes of Large Language Models Like Anthropic’s Claude

Researchers at Anthropic, focusing on interpretability within their large language model (LLM) development, acknowledge that these models are not sentient beings. However, analyzing and describing their functionalities often leads to comparisons with human cognitive processes, a challenge the team actively navigates while striving to understand how these complex systems operate. The recent release of two research papers, notably titled “On the Biology of a Large Language Model,” exemplifies this ongoing effort to demystify LLM behavior.

The increasing prevalence and sophistication of LLMs necessitates deeper investigation into their internal workings. Millions are already interacting with these technologies, a trend expected to intensify as models become more powerful. Anthropic’s research aims to “trace the thoughts” of large language models—a process that becomes increasingly vital as their capabilities grow and the mechanisms behind those capabilities remain opaque. As researcher Jack Lindsey explains, understanding the internal steps taken by a model is crucial for predicting and managing its output.

A key motivation for this interpretability work is to improve LLM safety and reliability. By gaining insight into how these models process information, developers can refine training methods to mitigate potential risks like unintentional data disclosure or the generation of harmful content. Previous research from Anthropic has demonstrated techniques analogous to analyzing human MRIs—visualizing neural activity—to identify conceptual understanding within an LLM. This work is now being extended to examine Claude’s specific processing steps, tracing how it transforms prompts into generated responses.

Recent studies have consistently revealed unexpected behaviors in LLMs, highlighting the complexity of their decision-making processes. One illustrative example involved observing Claude’s poetry generation. When prompted to complete a poem beginning “He saw a carrot and had to grab it,” Claude responded with “His hunger was like a starving rabbit.” Analysis of the model’s internal state revealed that the word “rabbit” appeared as a potential rhyme even *before* the line was generated, indicating an element of planning—a capability not initially anticipated in Claude’s design. This observation, noted by team lead Chris Olah, draws parallels to creative processes described by artists like Stephen Sondheim who have detailed their own methods for identifying and utilizing unexpected rhymes.

Original author: Steven Levy