首先来看相对简单的增加额外参数的方法,这种方法又称基于记忆或内存的模型编辑方法,代表方法 SERAC 最早出现于 Mitchell 提出“模型编辑”的论文,其核心思想在于保持模型原始参数不变,通过一个独立的参数集重新处理修改后的事实,具体而言,这类方法一般先增加一个“范围分类器”判断新输入是否处于被“重新编辑”过的事实...
论文解读——带你2分钟快速了解论文工作一、背景浙江大学著作的论文目的解决大模型的编辑问题。过去几年中出现了大量编辑大型语言模型的技术,其目标是在不对其他输入产生负面影响的情况下,有效地改变大型语言模型…
Large language models (LLMs) are profoundly useful for a vast array of difficult tasks. But they sometimes make unpredictable mistakes or perpetuate biased language. These sorts of errors tend to arise over time due to changes in the underlying d...
Large Language Models (LLMs) sometimes suffer from producing hallucinations, especially LLMs may generate untruthful responses despite knowing the correct knowledge. Activating the truthfulness within LLM is the key to fully unlocking LLM's knowledge potential. In this paper, we propose TruthX, an ...
This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs). This task seeks to adjust the models' responses to opinion-related questions on specified topics since an individual's personality often manifests in the form of their expressed ...
Source code for paper "TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space". TruthXis an inference-time method to elicit the truthfulness of LLMs by editing their internal representations in truthful space, thereby mitigating the hallucinations of LLMs. TruthX can ...
In this paper, we introduce Uni3D-LLM, a unified framework that leverages a Large Language Model (LLM) to integrate tasks of 3D perception, generation, and editing within point cloud scenes. This framework empowers users to effortlessly generate and modify objects at specified locations within a...
大模型(LLM)最新论文摘要 | Editing Personality for LLMs Authors: Shengyu Mao, Ningyu Zhang, Xiaohan Wang, Mengru Wang, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen This paper introduces an innovative task focused on editing the personality traits of Large Language Models (...
of Large Language Models" and "Knowledge Circuits in Pretrained Transformers" have been accepted by NeurIPS 2024. Our papers: "Knowledge Mechanisms in Large Language Models: A Survey and Perspective" and "Editing Conceptual Knowledge for Large Language Models" have been accepted by EMNLP 2024 ...
At Grammarly, we’re always exploring ways to make the writing and editing process better. This has included extensive use oflarge language models (LLMs), which got us wondering: What if we made LLMs specialize in text editing? Shape the way millions of people communicate!