Large Language Models (LLMs) are known for their human-like communication abilities, but the inner workings of these models remain somewhat of a mystery. Neural networks, including LLMs, are complex and deep, making it difficult to explain why a specific input leads to a particular output. Neural networks are often referred to as black boxes because the exact process of how they produce outputs is not easily understood.
Recent research has made progress in understanding LLMs by developing tools that can map and visualize the internal states of these models during computation. This allows for a better understanding of what the LLM is thinking when generating responses to prompts. By identifying and interpreting features within the neural activations, researchers can gain insights into the decision-making process of LLMs.
Features can represent various concepts or ideas, and understanding how these features are activated can provide valuable information about the model’s thought process. By mapping groups of activations to features, researchers can interpret the contents of the black box and measure the relationship between different features. This process can be visualized using heat maps to show the involvement of different features in the model’s responses.
Tools like Inspectus by labml.ai offer ways to visualize and understand the behavior of LLMs during processing, making these powerful models more transparent and useful. This research opens up new possibilities for manipulating and fine-tuning LLMs, especially in applications where operational clarity is essential. Overall, advancements in understanding the inner workings of LLMs are making these models more accessible and valuable for a wide range of applications.
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Source link: https://hackaday.com/2024/07/03/peering-into-the-black-box-of-large-language-models/