Large Language Models (LLMs)可大致划分为encoder-only LLMs(Bert)、encoder-decoder-based LLMs(T5)、decoder-only models(GPT、LLaMA)。最常见的 decoder-only 模型的训练策略如下: 预训练阶段:采用 causal language modeling 策略在超大量语料库上进行预训练,只关注前一个标记来自动回归预测下一个标记。 有监督...
2.2.1. Large Language Models 2.2.1.1. Transformers for LLM Transformer 是现代 LLMs 设计的基石,代表了与以前的序列学习方法相比重大转变。将 Transformer 作为编码器-解码器框架引入,其中编码器和解码器都由一系列相互堆叠的相同层组成。该架构中的每个模块都配备了一个自注意力模块和一个完全连接的前馈神经网络。
Knowledge editing for large language models can offer an efficient solution to alter a model's behavior without negatively impacting the overall performance. However, the current approaches encounter issues with limited generalizability across tasks, necessitating one distinct editor for each task, ...
Invited TalkEditing Large Language Models Advancing Machine Understanding and ControlGoogle Drive CCL2024 tutorial大语言模型知识机理、融合与编辑Google Drive&BaiduPan IJCAI2024 tutorialKnowledge Editing for Large Language ModelsGoogle Drive COLING2024 tutorialKnowledge Editing for Large Language ModelsGoogle Drive...
After the advent of ChatGPT, the readily available model developed by Open AI, large language models (LLMs) have become increasingly widespread, with many online users now accessing them daily to quickly get answers to their ...
2024-01-16, the EasyEdit has added the support for the precise model editing method PMET'AAAI24. 2024-01-03, we release a new paper:"A Comprehensive Study of Knowledge Editing for Large Language Models" with a new benchmark KnowEdit! KnowEdit is constructed by re-organizing and cleaning ...
Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge, leading to potentially outdated or inaccurate responses. This problem becomes even more challenging when dealing with multi-hop questions, since they require LLMs to upd...
That behaviour leads to an extreme and unpleasant end and the manager can be blamed for encouraging people to chat instead of work. There is a balance between the two. Knowledge transfer is facilitated when participants share a common language. A common language is shaped by the similar back...
DINM。去除大模型中黄暴的答案。将安全回答Ysafe和黄暴回答Yunsafe分别在预测阶段输入大模型,分析|Hsafe-Hunsafe|最大的那一层,认为是导致黄暴的结果,针对这一层重新sft训练,而固定其他层,训练时Loss加上正则 Lc=KL(PWt(·|[qcons;S])∥PW(·|[qcons;S]))使得修改前后的网络对qcons正常输入的概率变化不...
The repository for our paper: Neighboring Perturbations of Knowledge Editing on Large Language Models - mjy1111/PEAK