The article explores the challenges and advancements in understanding Large Language Models (LLMs), particularly focusing on the superposition phenomenon where multiple features share a single neuron, complicating interpretability. To address this, Sparse Autoencoders are introduced as a potential solution for disentangling features. The post highlights the concept of feature circuits—essentially how LLMs learn to synthesize inputs into complex outputs—using analogies from electronics. An example is provided of vision networks, illustrating how curve detectors contribute to more abstract shape recognition, setting the stage for an investigation into LLMs and subject-verb agreement tasks.
Superposition complicates the extraction of interpretable features from large language models (LLMs), as features often intermingle within single neurons, obscuring representation.
Sparse Autoencoders can help disentangle the mixed features in LLMs, allowing researchers to better understand how model components contribute to specific tasks.
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