研究人员正在质疑MIT CSAIL关于大型语言模型解释、修改和生成神经网络权重的说法。尽管研究结果可能具有重要意义，但批评者认为所使用的语言过于拟人化且不科学，歪曲了LLM的能力。该LLM似乎执行的是新颖的权重生成，而不是真正的优化或理解，这引发了对泛化能力和潜在机制的疑问。 AI
影响 挑战了当前对LLM能力和神经网络权重可解释性的理解，促使需要更严谨的科学语言。
排序理由 该集群讨论了研究发现及其解释，重点关注科学声明和潜在影响。
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The key problem is the vague, anthropomorphic language. Saying an LLM "understands" how to do something carries so much human baggage, and it's meaningless in the field of machine learning. What they showed was that an LLM could answer a few multiple choice questions about what t…
This post on the MIT CSAIL blog really upset me today. It's a fascinating and potentially important finding that throws doubt into many assumptions about how deep learning works, but it's claims are hyperbolic and anthropomorphizing to the point of being unscientific. Here they s…