于是,learn to learn 或者 hyper network 就诞生了——我们可以训练一个网络,来预测另一个网络的权重。 Editing Factual Knowledge in Language Models这篇文章设计了这种方法。 hyper network 更新参数 如上图所示,f是原始模型架构,\theta是原始模型参数,g是hyper network。g接收 原始输入、原始输出和目的输出,来预...
KnowledgeEditor Code for Editing Factual Knowledge in Language Models (https://arxiv.org/abs/2104.08164). @inproceedings{decao2021editing,title={Editing Factual Knowledge in Language Models},author={Nicola De Cao and Wilker Aziz and Ivan Titov},journal={Proceedings of the 2021 Conference on Empiric...
LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models Akshaj Kumar Veldanda, Shi-Xiong Zhang, Anirban Das, Supriyo Chakraborty, Stephen Rawls, Sambit Sahu, Milind Naphade. [paper] Meta-learning Editing Factual Knowledge in Language Models. (EMNLP 2021) ...
Neural language models (LMs) have been extensively trained on vast corpora to store factual knowledge about various aspects of the world described in texts. Current technologies typically employ knowledge editing methods or specific prompts to modify LM outputs. However, existing knowledge editing ...
We analyze the storage and recall of factual associations in autoregressive transformer language models, finding evidence that these associations correspond to localized, directly-editable computations. We first develop a causal intervention for identifying neuron activations that are decisive in a model's ...
Knowledge editing aims at updating knowledge of large language models (LLMs) to prevent them from becoming outdated. Existing work edits LLMs at the level of factual knowledge triplets. However, natural knowledge updates in the real world come from the occurrences of new events rather than direct...
Model editing techniques modify a minor proportion of knowledge in Large Language Models (LLMs) at a relatively low cost, which have demonstrated notable success. Existing methods assume Transformer Layer (TL) hidden states are values of key-value memories of the Feed-Forward Network (FFN). They...
Factual Knowledge Prompt stereotyping Semantic Robustness Toxicity Create a model evaluation job that uses human workers Automatic model evaluation Create an automatic model evaluation job in Studio Use the fmeval library to run an automatic evaluation Model evaluation results Job results Understand the res...
Large language models (LLMs) embed extensive knowledge and utilize it to perform exceptionally well across various tasks. Nevertheless, outdated knowledge or factual errors within LLMs can lead to misleading or incorrect responses, causing significant issues in practical applications. To rectify the fata...
The locate-then-edit paradigm has shown significant promise for knowledge editing (KE) in Large Language Models (LLMs). While previous methods perform well on single-hop fact recall tasks, they consistently struggle with multi-hop factual recall tasks involving newly edited knowledge. In this paper...