This work introduces LAB (Large-scale Alignment for chatBots), a novel
methodology designed to overcome the scalability challenges in the
instruction-tuning phase of large language model (LLM) training. Leveraging a
taxonomy-guided synthetic data generation process and a multi-phase tuning
framework, LAB significantly reduces reliance on expensive human annotations
and proprietary models like GPT-4. We demonstrate that LAB-trained models can
achieve competitive performance across several benchmarks compared to models
trained with traditional human-annotated or GPT-4 generated synthetic data.
Thus offering a scalable, cost-effective solution for enhancing LLM
capabilities and instruction-following behaviors without the drawbacks of
catastrophic forgetting, marking a step forward in the efficient training of
LLMs for a wide range of applications.

使用 LAB（大规模对话机器人对齐）方法解决大型语言模型（LLM）训练中的可扩展性挑战，通过基于分类法的合成数据生成和多阶段调整框架，减少人工标注和专有模型的依赖，LAB 训练的模型在多个基准测试上表现出与传统人工标注或 GPT-4 生成的合成数据训练模型相媲美的性能，为提高 LLM 能力和指令遵循行为提供了可扩展、经济高效的解决方案，避免了灾难性遗忘的弊端，为各种应用的 LLM 的高效训练迈出了一步。

LAB：ChatBots 的大规模对齐

LAB: Large-Scale Alignment for ChatBots

Image harmonization is an essential step in image composition that adjusts
the appearance of composite foreground to address the inconsistency between
foreground and background. Existing methods primarily operate in correlated
$RGB$ color space, leading to entangled features and limited representation
ability. In contrast, decorrelated color space (e.g., $Lab$) has decorrelated
channels that provide disentangled color and illumination statistics. In this
paper, we explore image harmonization in dual color spaces, which supplements
entangled $RGB$ features with disentangled $L$, $a$, $b$ features to alleviate
the workload in harmonization process. The network comprises a $RGB$
harmonization backbone, an $Lab$ encoding module, and an $Lab$ control module.
The backbone is a U-Net network translating composite image to harmonized
image. Three encoders in $Lab$ encoding module extract three control codes
independently from $L$, $a$, $b$ channels, which are used to manipulate the
decoder features in harmonization backbone via $Lab$ control module. Our code
and model are available at
\href{https://github.com/bcmi/DucoNet-Image-Harmonization}{this https URL}.

本文研究了基于双色彩空间的图像协调方法，将混合的 RGB 特征与解开的 Lab 特征相结合，减轻协调过程中的负担。网络包括一个 RGB 协调主干，一个 Lab 编码模块和一个 Lab 控制模块。