Originally published at ssojet
Two recent papers from Anthropic explore the inner workings of large language models (LLMs) by developing an approach called the "AI Microscope." This tool aims to locate interpretable concepts and link them to computational circuits that translate these concepts into language. The internal mechanisms of LLMs remain poorly understood, complicating the explanation of their problem-solving strategies, which are embedded in billions of computations.
The AI Microscope replaces the model under study with a "replacement model," where neurons are substituted with sparsely-active features representing interpretable concepts. For instance, a feature may activate when the model generates a state capital. The researchers create a local replacement model for each prompt, which produces identical outputs to the original model while maximizing computation replacement with features.
The researchers also constructed an attribution graph to describe the flow of features from the initial prompt to the output by pruning away non-influential features. This approach led to findings about multilingual capabilities, suggesting that Claude, the LLM in question, possesses a universal language to generate concepts before translating them into specific languages.
Moreover, the research challenges the notion that LLMs generate output without forethought. Analysis of how Claude creates rhymes revealed that it plans ahead, considering potential rhyming words before composing a line. Additionally, the research delves into the phenomenon of "hallucination," where models generate false information. This occurs due to the interplay between features that identify known entities and those that indicate uncertainty.
For more insights, refer to the original papers on transformer-circuits, Claude's behaviors, and the challenges of LLM interpretability.
Circuit Tracing in LLMs
Anthropic's recent advancements include a technique called circuit tracing, which allows researchers to track decision-making processes within LLMs step by step. This method has revealed counterintuitive strategies that LLMs use to complete sentences, solve math problems, and manage hallucinations. The findings challenge basic assumptions about LLMs and expose their weaknesses.
The research found that Claude uses components independent of language to answer questions, activating concepts related to "smallness" and "opposites" before selecting a specific language for the response. In addition, the study observed that Claude employs unique internal strategies for solving math problems, which differ from known methods in its training data.
Furthermore, when asked about its reasoning processes, Claude often provides explanations that do not accurately reflect its internal workings, a behavior that suggests LLMs may fabricate rationalizations similar to human reasoning.
For a deeper dive into circuit tracing, see the detailed reports on Anthropic's research and its implications for understanding LLM operations.
Implications for AI and IAM Solutions
Understanding the inner workings of LLMs, like Claude, has significant implications for industries relying on automated reasoning and user management. At SSOJet, we recognize the importance of secure authentication and user management in the context of developing advanced AI solutions. Our API-first platform offers secure single sign-on (SSO), multi-factor authentication (MFA), and passkey solutions, ensuring that enterprises can manage user identities effectively while maintaining high-security standards.
The insights gained from Anthropic's research emphasize the need for robust security frameworks in AI applications, particularly as LLMs become increasingly integrated into business processes. Implementing secure SSO with SSOJet's platform can help organizations mitigate risks associated with data breaches and unauthorized access.
Explore our offerings at SSOJet to enhance your enterprise's authentication and user management systems.