Understanding how machine learning models respond to distributional shifts is
a key research challenge. Mazes serve as an excellent testbed due to varied
generation algorithms offering a nuanced platform to simulate both subtle and
pronounced distributional shifts. To enable systematic investigations of model
behavior on out-of-distribution data, we present $\texttt{maze-dataset}$, a
comprehensive library for generating, processing, and visualizing datasets
consisting of maze-solving tasks. With this library, researchers can easily
create datasets, having extensive control over the generation algorithm used,
the parameters fed to the algorithm of choice, and the filters that generated
mazes must satisfy. Furthermore, it supports multiple output formats, including
rasterized and text-based, catering to convolutional neural networks and
autoregressive transformer models. These formats, along with tools for
visualizing and converting between them, ensure versatility and adaptability in
research applications.

为了系统地研究机器学习模型对分布变化的响应，我们提出了「maze-dataset」，这是一个生成、处理和可视化迷宫求解任务数据集的全面库，支持多种输出格式和工具，保证在研究应用中具有多样性和适应性。

一款用于生成和操作迷宫数据集的可配置库

A Configurable Library for Generating and Manipulating Maze Datasets

Likelihood, although useful as a training loss, is a poor search objective
for guiding open-ended generation from language models (LMs). Existing
generation algorithms must avoid both unlikely strings, which are incoherent,
and highly likely ones, which are short and repetitive. We propose contrastive
decoding (CD), a more reliable search objective that returns the difference
between likelihood under a large LM (called the expert, e.g. OPT-13b) and a
small LM (called the amateur, e.g. OPT-125m). CD is inspired by the fact that
the failures of larger LMs are even more prevalent in smaller LMs, and that
this difference signals exactly which texts should be preferred. CD requires
zero training, and produces higher quality text than decoding from the larger
LM alone. It also generalizes across model types (OPT and GPT2) and
significantly outperforms four strong decoding algorithms in automatic and
human evaluations.

本文提出一种基于对比解码的生成算法，将大型语言模型与小型语言模型相比较，找出高质量的文本生成结果，并在自动和人工评估中优于四种强大的解码算法。