We use GPT-4 to automatically write explanations for the behavior of neurons in large language models and to score those explanations. We release a dataset of these (imperfect) explanations and scores for every neuron in GPT-2. Language models have become more capable and more broadly deployed,...
Explain: explainer model 接收神经元对于文本段的(token,activation)来对神经元的激活作出解释 Simulate: 使用simulator model 来模拟(预测)神经元的激活 Score: 根据模拟激活与真实激活的匹配程度自动对解释进行评分 Step 1: 生成神经元行为的解释 构建prompt给予explainer model去生成解释 prompt的构成描述: 基本任务描...
141.Language models can explain neurons in language modelsOpenAI的用GPT-4解释GPT-2的论文(虽说是论文,但其实是一个网页)。步骤也并不复杂:(1)对于被给定的GPT-2的神经元和输出序列,让GPT-4解释这个神经元可能起什么作用;(2)用GPT-4模拟这个神经元会做什么;(3)与真实的激活情况作对比,得出GPT-4的判断...
we note that like in other multitasking models, units in our sensorimotor-RNNs exhibited functional clustering, where similar subsets of neurons show high variance across similar sets of tasks (Supplementary Fig.7). Moreover, we found that models can learn unseen tasks by only training...
But ordinary language, augmented with symbolic logic, with geometric diagrams, with mathematical equations that describe physical theories, and with computer models and simulations, can describe the knowledge that best explains how the world works, that is, science. This augmented language of science ...
With incredible speed Large Language Models (LLMs) are reshaping many aspects of society. This has been met with unease by the public, and public discourse is rife with questions about whether LLMs are or might be conscious. Because there is widespread d
which only explain the reasoning behind an individual prediction, andglobalexplainers, which instead provide a rationale for the whole dataset [26]. XAI approaches can be further categorized intopost-hocandinterpretable-by-designmethods. Post-hoc methods aim at interpreting black-box models after trai...
That was a question to which I had to give a lot of consideration. I think the best way to explain it is to explain our thought process, which is kind of like a recipe. Thought Process Recipe: 1 part passion 1 part competitiveness ...
Two different models have emerged to explain the interaction between L1 and L2 when a bilingual is speaking in only one (target) lan- guage. In the first group of language-specific selection models, it is thought that both languages may be active but bilinguals develop the ability to ...
can be found at the GitHub repository (see the Data and Code Availability section). Third, polyBERT masks 15% (default parameter for masked language models) of the tokens to create a self-supervised training task. In this training task, polyBERT is taught to predict the masked tokens using...