The video explores the challenges of mechanistic interpretability in large language models (LLMs) like ChatGPT and Google’s Gemini, highlighting the difficulty in understanding their underlying concepts and behaviors, which are often likened to “dark matter.” It discusses the use of sparse autoencoders to extract human-understandable features from these models, while emphasizing the complexities and unknowns that remain in fully grasping their inner workings and ensuring alignment with human values.
The video discusses the challenges of understanding large language models (LLMs) like ChatGPT and Google’s Gemini, particularly focusing on the concept of mechanistic interpretability. It highlights the difficulty in determining whether these models are being truthful or deceptive, especially when users ask them to forget specific phrases. Despite training these models to be helpful and honest, researchers lack direct access to the underlying concepts and behaviors that govern their responses. This gap in understanding is likened to “dark matter” in the universe of model interpretability, where only a small fraction of the model’s learned concepts can be extracted and understood.
One promising approach to interpretability involves using sparse autoencoders to extract features from LLMs. These features often correspond to human-understandable concepts, such as animals or more complex ideas like internal conflict. By manipulating these features, researchers can influence the model’s behavior, such as increasing its admission of uncertainty when asked to forget a phrase. However, the authors note that only a tiny portion of the model’s concepts has been successfully extracted, emphasizing the vast unknowns that remain.
The video provides a walkthrough of how a phrase is processed through the Gemini model, detailing the transformation of input tokens into matrices as they pass through multiple layers. Each layer consists of attention and multi-layer perceptron blocks that modify the input, ultimately leading to a probability distribution for the next token. The model’s responses can vary significantly based on the probabilities assigned to different tokens, illustrating the nuanced decision-making process behind its outputs.
As the video progresses, it delves into the concept of polys semanticity, where individual neurons in the model may correspond to multiple, seemingly unrelated concepts. This complicates the task of isolating specific behaviors or concepts within the model. The authors discuss efforts to understand these relationships better, including the use of sparse autoencoders to map neuron outputs to distinct concepts, although challenges remain in identifying which combinations of neurons correspond to specific ideas.
In conclusion, while mechanistic interpretability and sparse autoencoders have provided valuable insights into LLMs, significant obstacles remain in fully understanding their inner workings. The video suggests that as researchers continue to explore these models, they may uncover more about the vast array of concepts they contain, but the complexity of these systems may outpace our ability to interpret them. The ongoing research in this field is crucial for improving our understanding of AI behavior and ensuring that these models align with human values and expectations.
