Source URL: https://simonwillison.net/2025/Mar/27/tracing-the-thoughts-of-a-large-language-model/
Source: Simon Willison’s Weblog
Title: Tracing the thoughts of a large language model
Feedly Summary: Tracing the thoughts of a large language model
In a follow-up to the research that brought us the delightful Golden Gate Claude last year, Anthropic have published two new papers about LLM interpretability:
Circuit Tracing: Revealing Computational Graphs in Language Models extends last year’s interpretable features into attribution graphs, which can “trace the chain of intermediate steps that a model uses to transform a specific input prompt into an output response".
On the Biology of a Large Language Model uses that methodology to investigate Claude 3.5 Haiku in a bunch of different ways. Multilingual Circuits for example shows that the same prompt in three different languages uses similar circuits for each one, hinting at an intriguing level of generalization.
To my own personal delight, neither of these papers are published as PDFs. They’re both presented as glorious mobile friendly HTML pages with linkable sections and even some inline interactive diagrams. More of this please!
Tags: anthropic, claude, pdf, generative-ai, ai, llms
AI Summary and Description: Yes
Summary: The text discusses recent research from Anthropic focusing on the interpretability of large language models (LLMs), highlighting two new papers that explore computational graphs and multilingual processing within LLMs. This research contributes to the understanding of how these models generate responses, which is crucial for AI security and transparency.
Detailed Description: The provided text outlines significant advancements in the field of large language models, particularly regarding their interpretability by Anthropic. The papers discussed introduce important methodologies and findings that have implications for AI security, a relevant category given the increasing concerns about the transparency and accountability of AI systems.
* Key Points:
– **Research Papers**: Anthropic published two papers that focus on improving the interpretability of LLMs.
– **Circuit Tracing**: This study explores “attribution graphs” to trace how models convert input prompts into responses, offering insights into model behavior.
– **On the Biology of a Large Language Model**: This paper utilizes the tracing methodology to analyze Claude 3.5, particularly examining multilingual capabilities and generalization across different languages.
– **Format Innovation**: Both papers are presented as interactive HTML pages, enhancing accessibility and engagement for researchers and practitioners in the field.
* Implications for Professionals:
– The findings on interpretability are vital for enhancing trust in AI systems, a growing concern among security professionals managing AI integration.
– Understanding the computational processes behind model outputs can aid in identifying potential biases and security vulnerabilities within LLMs.
– The interactive presentation format can serve as a model for future publications, promoting better communication of complex AI-related research.
Overall, this text is particularly relevant for those involved in AI security, interpretability, and compliance, as it addresses the need for transparent AI systems in a landscape increasingly focused on ethical and secure AI deployment.