Training with mixed data distributions is a common and important part of
creating multi-task and instruction-following models. The diversity of the data
distributions and cost of joint training makes the optimization procedure
extremely challenging. Data mixing methods partially address this problem,
albeit having a sub-optimal performance across data sources and require
multiple expensive training runs. In this paper, we propose a simple and
efficient alternative for better optimization of the data sources by combining
models individually trained on each data source with the base model using basic
element-wise vector operations. The resulting model, namely Distribution Edited
Model (DEM), is 11x cheaper than standard data mixing and outperforms strong
baselines on a variety of benchmarks, yielding up to 6.2% improvement on MMLU,
11.5% on BBH, 16.1% on DROP, and 9.3% on HELM with models of size 3B to 13B.
Notably, DEM does not require full re-training when modifying a single
data-source, thus making it very flexible and scalable for training with
diverse data sources.

提出了一种简单和高效的方法，使用基本的逐元素向量操作将每个数据源上单独训练的模型与基本模型相结合，从而更好地优化数据源，得到一种名为分布编辑模型（DEM）的模型，与标准数据混合相比廉价 11 倍，并在各种基准测试中优于强基准，对大小为 3B 到 13B 的模型分别可以获得 MMLU 的 6.2％改进，BBH 的 11.5％改进，DROP 的 16.1％，HELM 的 9.3％改进。值得注意的是，DEM 在修改单个数据源时不需要完全重新训练，因此非常灵活和可扩展以适应各种数据源的训练。

DEM：混合数据分布训练的分布编辑模型

DEM: Distribution Edited Model for Training with Mixed Data  Distributions

Recent advancements in instruction-following models have made user
interactions with models more user-friendly and efficient, broadening their
applicability. In graphic design, non-professional users often struggle to
create visually appealing layouts due to limited skills and resources. In this
work, we introduce a novel multimodal instruction-following framework for
layout planning, allowing users to easily arrange visual elements into tailored
layouts by specifying canvas size and design purpose, such as for book covers,
posters, brochures, or menus. We developed three layout reasoning tasks to
train the model in understanding and executing layout instructions. Experiments
on two benchmarks show that our method not only simplifies the design process
for non-professionals but also surpasses the performance of few-shot GPT-4V
models, with mIoU higher by 12% on Crello. This progress highlights the
potential of multimodal instruction-following models to automate and simplify
the design process, providing an approachable solution for a wide range of
design tasks on visually-rich documents.

近期，在指令追踪模型方面取得的进展使得用户与模型的交互更加用户友好和高效，扩大了它们的适用范围。本研究引入了一种新颖的多模态指令追踪框架，用于布局规划，在设计领域，非专业用户经常由于技能和资源有限而难以创建具有视觉吸引力的布局。我们提出了三个布局推理任务来训练模型理解和执行布局指令。在两个基准测试中的实验证明，我们的方法不仅简化了非专业人员的设计过程，而且在 Crello 上的 mIoU 高于 few-shot GPT-4V 模型 12%。这一进展凸显了多模态指令追踪模型在自动化和简化设计过程方面的潜力，为视觉丰富文档上的各类设计任务提供了可行的解决方案。

视觉丰富文档的自动布局规划与指导模型

Automatic Layout Planning for Visually-Rich Documents with  Instruction-Following Models

Large language models~(LLMs) obtain instruction-following capability through
instruction-finetuning (IFT) on supervised instruction/response data. However,
widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly contain many
low-quality instances with incorrect or irrelevant responses, which are
misleading and detrimental to IFT. In this paper, we propose a simple and
effective data selection strategy that automatically identifies and removes
low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce
AlpaGasus, which is finetuned on only 9k high-quality data filtered from the
52k Alpaca data. AlpaGasus significantly outperforms the original Alpaca as
evaluated by GPT-4 on multiple test sets and its 13B variant matches $>90\%$
performance of its teacher LLM (i.e., Text-Davinci-003) on test tasks. It also
provides 5.7x faster training, reducing the training time for a 7B variant from
80 minutes (for Alpaca) to 14 minutes \footnote{We apply IFT for the same
number of epochs as Alpaca(7B) but on fewer data, using 4$\times$NVIDIA A100
(80GB) GPUs and following the original Alpaca setting and hyperparameters.}.
Overall, AlpaGasus demonstrates a novel data-centric IFT paradigm that can be
generally applied to instruction-tuning data, leading to faster training and
better instruction-following models. Our project page is available at:
https://lichang-chen.github.io/AlpaGasus/.

本研究提出了一种数据选取策略，利用一个强大的大型语言模型（ChatGPT）自动地识别和删除低质量的数据，从而过滤出高质量的数据并训练指令跟随模型。通过使用该策略，作者利用仅有的 9k 条高质量数据训练出了一个新的大型语言模型 AlpaGasus，相比于 Alpaca 的 52k 数据集，在多个测试集上获得了更好的表现。同时，AlpaGasus 提供了 5.7 倍更快的训练速度，训练时间从 Alpaca 的 80 分钟减少到了 14 分钟。该方法展示了一种数据中心的指令跟随模型训练范式，可广泛应用于指令调整数据，提高训练效率和准确率。