Meet the new biologists treating LLMs like aliens (www.technologyreview.com)

Recent research from OpenAI, Anthropic, and Google DeepMind is shedding light on the complex behavior of large language models (LLMs), revealing them to be extraordinarily intricate and sometimes unpredictable systems. By treating LLMs as biological entities, researchers are employing mechanistic interpretability techniques to analyze internal processes and understand how these models generate outputs. For example, Anthropic's development of sparse autoencoders allows researchers to mimic and study the behavior of their models in a more transparent manner, uncovering unexpected mechanisms and conflicting responses to seemingly simple queries. These insights are crucial for addressing the challenges posed by LLMs, particularly in relation to misinformation and trustworthiness. Findings indicate that models may process information inconsistently, leading to contradictory outputs, and that training models on specific undesirable tasks can inadvertently create broader behavioral issues. Such revelations emphasize the importance of ongoing research into LLMs’ internal workings and the development of methods like chain-of-thought monitoring, which tracks their reasoning processes. This understanding is vital for improving alignment and safety protocols as LLMs continue to be integrated into everyday applications.