论文

本文提出一种多任务深度学习的原则性方法，通过考虑各任务的同方差不确定性来加权多个损失函数，从而在分类与回归场景下同时学习具有不同单位或尺度的多种量。A principled approach to multi-task deep learning is proposed which weighs multiple loss functions by considering the homoscedastic uncertainty of each task, allowing us to simultaneously learn various quantities with different units or scales in both classification and regression settings.

结果表明，使用人类反馈进行微调是使语言模型与人类意图对齐的一个有前景的方向，在真实性方面有所提升，并减少了有毒输出的生成，同时在公开 NLP 数据集上的性能回归极小。The results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent and showing improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.

在三种大语言模型上的实验表明，思维链提示能够在一系列算术、常识和符号推理任务上提升性能。Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks.

本工作提出两种参数削减技术以降低 BERT 的内存占用并提升训练速度，并采用一种聚焦于建模句子间连贯性的自监督损失。This work presents two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT, and uses a self-supervised loss that focuses on modeling inter-sentence coherence.

本工作提出 BERTScore——一种文本生成自动评估指标，与人类判断的相关性更强，并在模型选择性能上优于现有指标。This work proposes BERTScore, an automatic evaluation metric for text generation that correlates better with human judgments and provides stronger model selection performance than existing metrics.

研究表明，利用变分方法最新进展的深度生成模型与近似贝叶斯推断能够带来显著提升，使生成式方法在半监督学习上极具竞争力。It is shown that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.

本工作提出一种通过引入自注意力来提取可解释句子嵌入的新模型，使用一个二维矩阵表示嵌入，其中矩阵的每一行关注句子的不同部分。A new model for extracting an interpretable sentence embedding by introducing self-attention is proposed, which uses a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.

本工作指出，这些局部 patch 内部的注意力同样是构建高性能视觉 Transformer 的关键，并探索了一种新架构，即 Transformer iN Transformer (TNT)。It is pointed out that the attention inside these local patches are also essential for building visual transformers with high performance and a new architecture, namely, Transformer iN Transformer (TNT), is explored.

本文详细阐述了驱动 PyTorch 实现的原则及其在架构中的体现，并解释了 runtime 关键组件的精心且务实的实现如何使其协同工作以获得出色的性能。This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.

这项系统性研究在数十项语言理解任务上比较了预训练目标、架构、无标注数据集、迁移方法及其他因素，并在涵盖摘要、问答、文本分类等的许多基准上取得了 SOTA 结果。This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks and achieves state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more.

本文提出了一种基于迭代模型平均的深度网络联邦学习实践方法，并进行了广泛的实证评估，考虑了五种不同的模型架构和四个数据集。This work presents a practical method for the federated learning of deep networks based on iterative model averaging, and conducts an extensive empirical evaluation, considering five different model architectures and four datasets.

本文提出了 mixup，一种通过对样本对及其标签的凸组合来训练神经网络的简单学习原则，提升了 SOTA 神经网络架构的泛化能力。This work proposes mixup, a simple learning principle that trains a neural network on convex combinations of pairs of examples and their labels, which improves the generalization of state-of-the-art neural network architectures.

本文提出了一种通用的无监督学习方法——对比预测编码（Contrastive Predictive Coding），用于从高维数据中提取有用的表征，并在语音、图像、文本和 3D 环境中的强化学习四个不同领域取得了出色的性能。This work proposes a universal unsupervised learning approach to extract useful representations from high-dimensional data, which it calls Contrastive Predictive Coding, and demonstrates that the approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments.

本文提出了一种双时间尺度更新规则（TTUR），用于在任意 GAN 损失函数下使用 SGD 训练 GAN，并引入了 Frechet Inception Distance（FID），相比 Inception Score 能更好地捕捉生成图像与真实图像之间的相似性。This work proposes a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions and introduces the "Frechet Inception Distance" (FID) which captures the similarity of generated images to real ones better than the Inception Score.

名为 PaLM 的 540 亿参数、密集激活的 Transformer 语言模型取得了突破性性能，在一系列多步推理任务上超越了微调后的 SOTA，并在最近发布的 BIG-bench 基准上超越了人类平均水平。A 540-billion parameter, densely activated, Transformer language model, which is called PaLM achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark.

本文提出了一种通过创建在所有边缘设备之间全局共享的小型数据子集来改进非独立同分布数据训练的策略，并表明在 CIFAR-10 数据集上，仅共享 5% 的全局数据即可将准确率提升 30%。This work presents a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices, and shows that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.

采用迭代的在线训练模式，按周节奏用新的人类反馈数据更新偏好模型与 RL 策略，并发现 RL 奖励与策略相对其初始化的 KL 散度平方根之间近似呈线性关系。An iterated online mode of training, where preference models and RL policies are updated on a weekly cadence with fresh human feedback data, and a roughly linear relation between the RL reward and the square root of the KL divergence between the policy and its initialization is identified.

研究表明，APE 生成的提示词既可引导模型趋向真实性和/或信息量，也可通过将其前置拼接到标准上下文学习提示词之前来提升少样本学习性能。It is shown that APE-engineered prompts can be applied to steer models toward truthfulness and/or informativeness, as well as to improve few-shot learning performance by simply prepending them to standard in-context learning prompts.

本文提出 Atlas，一个经过精心设计并预训练的检索增强大语言模型，能以极少训练样例学习知识密集型任务，并研究了文档索引内容的影响，表明该索引可便捷地更新。This work presents Atlas, a carefully designed and pre-trained retrieval augmented language model able to learn knowledge intensive tasks with very few training examples, and studies the impact of the content of the document index, showing that it can easily be updated.

本文通过深度多模态嵌入视觉与自然语言数据，提出了一种用于图文双向检索的模型，并引入结构化的最大间隔目标，使该模型能够显式地跨模态关联片段。This work introduces a model for bidirectional retrieval of images and sentences through a deep, multi-modal embedding of visual and natural language data and introduces a structured max-margin objective that allows this model to explicitly associate fragments across modalities.

论文证明，使用标注数据进行微调，并允许模型查询外部知识源，能够在安全性和事实性这两个关键挑战上带来显著提升。It is demonstrated that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.

本文利用源自 CNN 的同一基础信息重新审视初始搜索与重排序两个检索阶段，显著改进了现有基于 CNN 的识别流水线。This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN, and significantly improves existing CNN-based recognition pipeline.

通过引入扩散卷积运算，本文展示了如何从图结构数据中学习基于扩散的表示，并将其作为节点分类的有效基础。Through the introduction of a diffusion-convolution operation, it is shown how diffusion-based representations can be learned from graph-structured data and used as an effective basis for node classification.

Manifold Mixup 在监督学习、对单步对抗攻击的鲁棒性、半监督学习以及留出样本的负对数似然（NLL）上，相较强基线均取得了大幅提升。Manifold Mixup achieves large improvements over strong baselines in supervised learning, robustness to single-step adversarial attacks, semi-supervised learning, and Negative Log-Likelihood on held out samples.

本文提出新的计算机视觉基础模型 Florence，通过融入来自 Web 规模图文数据的通用视觉-语言表示，将表征范围从粗粒度（场景）扩展到细粒度、从静态（图像）扩展到动态（视频）、从 RGB 扩展到多种模态（描述、深度等）。This work introduces a new computer vision foundation model, Florence, to expand the representations from coarse (scene) to fine, from static (images) to dynamic (videos), and from RGB to multiple modalities (caption, depth), by incorporating universal visual-language representations from Web-scale image-text data.

本文提出了不变风险最小化（IRM），一种用于在多个训练分布上估计不变相关性的学习范式，并展示了 IRM 学到的不变性如何与数据的因果结构相关，从而实现分布外泛化。This work introduces Invariant Risk Minimization, a learning paradigm to estimate invariant correlations across multiple training distributions and shows how the invariances learned by IRM relate to the causal structures governing the data and enable out-of-distribution generalization.

研究表明 BigBird 是序列函数的通用逼近器，且具备图灵完备性，从而保留了二次全注意力模型的这些性质。It is shown that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model.

本工作研究图结构输入的特征学习技术，并在程序验证任务上取得 SOTA 性能，该任务需将子图与抽象数据结构进行匹配。This work studies feature learning techniques for graph-structured inputs and achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.

本工作提出在海量文本语料上使用新的自监督目标 PEGASUS 对大型 Transformer 编码器-解码器模型进行预训练，并证明其在所有 12 个下游数据集上按 ROUGE 分数衡量均取得 SOTA 性能This work proposes pre-training large Transformer-based encoder-decoder models on massive text corpora with a new self-supervised objective, PEGASUS, and demonstrates it achieves state-of-the-art performance on all 12 downstream datasets measured by ROUGE scores.

尽管方法简单，Decision Transformer 在 Atari、OpenAI Gym 和 Key-to-Door 任务上达到或超过 SOTA 无模型离线 RL 基线的性能Despite its simplicity, Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks.

提出了能够平衡"延迟告警的上下文敏感成本"与"打断成本"的模型与推理流程，并通过对用户活动与通知内容的分析，描述了在不确定性下推理此类成本所面临的挑战Models and inference procedures that balance the context-sensitive costs of deferring alerts with the cost of interruption are presented and the challenge of reasoning about such costs under uncertainty via an analysis of user activity and the content of notifications is described.

一个仅依赖字符级输入的简单神经语言模型，仅从字符即可编码语义和正字法信息，表明在许多语言中，字符输入足以完成语言建模。A simple neural language model that relies only on character-level inputs that is able to encode, from characters only, both semantic and orthographic information and suggests that on many languages, character inputs are sufficient for language modeling.

描述了一种无监督学习通用分布式句子编码器的方法，利用书籍文本的连续性，训练编码器-解码器模型以重建编码段落的周围句子。The approach for unsupervised learning of a generic, distributed sentence encoder is described, using the continuity of text from books to train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage.

本工作训练了一个预测的计算最优模型 Chinchilla，使用与 Gopher 相同的计算预算，但参数量为 70B、数据量为 4 倍，达到 SOTA 平均准确率，比 Gopher 提升超过 7%。This work trains a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\times$ more more data, and reaches a state-of-the-art average accuracy, greater than a 7% improvement over Gopher.

本工作综述了可用于推断用户需求的贝叶斯用户模型研究，这些模型综合考虑用户的背景、操作和查询，并提出了一种智能用户界面的整体架构。This work reviews work on Bayesian user models that can be employed to infer a user's needs by considering a users' background, actions, and queries and proposes an overall architecture for an intelligent user interface.

探索以交错方式使用 LLM 同时生成推理轨迹和任务特定动作，使两者产生更大协同：推理轨迹帮助模型归纳、跟踪和更新动作计划以及处理异常，而动作使其与外部源交互以获取额外信息。The use of LLMs are explored to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources to gather additional information.