Demystifying the Black Box: A Deep Dive into LLM Interpretability
The concerns, possible solutions, and trends in LLM Interpretability space.
∘ Current Concerns:
∘ Why We Need LLM Interpretability:
∘ The Interpretability Landscape:
∘ Current Limitations:
∘ Future Direction:
∘ Conclusion:
∘ Paper References:
Large Language Models (LLMs) have exploded onto the scene, captivating us with their ability to generate human-quality text, translate languages, and even write poetry. But as these complex models weave their way into our lives, a crucial question looms: can we understand how they work? This question forms the core of LLM interpretability, a fascinating and rapidly evolving field with profound implications for the future of AI.
Current Concerns:
The inherent complexity of LLMs, often comprising millions or even billions of parameters, makes it challenging to decipher how they process and interpret information. The lack of interpretability poses several concerns:
- Black-box Nature: LLMs are often considered as “black boxes” due to the difficulty in understanding the internal workings of the model. This opacity raises questions…
