Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation). For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints, which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.

大型语言模型已被证明可以使用少量学习来实现各种自然语言任务的出色表现，这大大减少了将模型调整到特定应用程序所需的特定任务培训示例的数量。为了进一步了解量表对少量学习的影响，我们培训了一个5400亿个参数，密集激活的变压器语言模型，我们称之为“途径”语言模型棕榈。我们使用Pathways在6144 TPU V4芯片上训练了Palm，这是一种新的ML系统，可在多个TPU POD上进行高效的训练。我们通过在数百种语言理解和产生基准的基准方面实现最先进的学习结果来证明扩展的持续好处。在这些任务中，Palm 540B实现了突破性的表现，在一系列多步推理任务上表现出色，超过了最新的最新表现，并且在最近发布的Big Benchmark上表现优于平均人类表现。大量的大型基础任务显示出与模型量表的不连续改进，这意味着当我们扩展到最大模型时，性能急剧增加。 Palm在多语言任务和源代码生成方面也具有很强的功能，我们在各种基准测试中证明了这一点。我们还提供了有关偏见和毒性的全面分析，并研究了训练数据记忆的程度，相对于模型量表。最后，我们讨论与大语言模型有关的道德考虑，并讨论潜在的缓解策略。

机器学习已成功应用于系统应用程序（如内存预取和缓存），其中已知模型已显示出优于HeuRistics。然而，缺乏了解这些模型的内部工作 - 可解释性 - 仍然是在现实世界部署中采用的主要障碍。了解模型的行为可以帮助系统管理员，开发人员在模型中获得信心，了解生产中的风险和调试意外行为。计算机系统中使用的模型的可解释性构成了特定的挑战：与在图像或文本上培训的ML模型不同，输入域（例如，内存访问模式，程序计数器）不是立即解释的。因此，一项重大挑战是在对人类从业者达成的概念方面解释该模型。通过分析最先进的高速缓存模型，我们提供了证据表明该模型已经学习超出可以利用解释的简单统计数据的概念。我们的工作为系统ML模型的解读提供了第一步，并突出了这一新兴研究区域的承诺和挑战。

具有更多数据，计算和参数的缩放语言模型在自然语言处理方面取得了重大进展。例如，由于缩放，GPT-3能够在内心学习任务上实现强烈结果。但是，培训这些大密度模型需要大量的计算资源。在本文中，我们提出并开发了名为Glam（通用语言模型）的语言模型系列，它使用稀疏激活的专家架构来规模模型容量，同时与致密变体相比，也产生显着更少的训练成本。最大的Glam具有1.2万亿参数，比GPT-3大约为7倍。它仅消耗了用于训练GPT-3的1/3的能量，并且需要一半的计算拖鞋进行推理，同时仍然在29个NLP任务中实现更好的整体零射击和一次性性能。

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

在边缘计算中，必须根据用户移动性迁移用户的服务配置文件。已经提出了强化学习（RL）框架。然而，这些框架并不考虑偶尔的服务器故障，尽管很少会阻止Edge Computing用户的延迟敏感应用程序（例如自动驾驶和实时障碍物检测）的平稳和安全功能，因为用户的计算作业不再是完全的。由于这些故障的发生率很低，因此，RL算法本质上很难为数据驱动的算法学习针对典型事件和罕见事件方案的最佳服务迁移解决方案。因此，我们引入了罕见的事件自适应弹性框架火，该框架将重要性采样集成到加强学习中以放置备份服务。我们以与其对价值函数的贡献成正比的稀有事件进行采样，以学习最佳政策。我们的框架平衡了服务迁移和迁移成本之间的迁移权衡，与失败的成本以及备份放置和移民的成本。我们提出了一种基于重要性抽样的Q-学习算法，并证明其界限和收敛到最佳性。随后，我们提出了新的资格轨迹，我们的算法的线性函数近似和深Q学习版本，以确保其扩展到现实世界情景。我们扩展框架，以适应具有不同风险承受失败的用户。最后，我们使用痕量驱动的实验表明我们的算法在发生故障时会降低成本。

本报告介绍了第七次机器翻译会议（WMT22）的通用机器翻译任务的自动评估。它总共评估了21个翻译方向的185个系统，包括高资源至低资源语言对以及与遥远语言密切相关的系统。这种大规模的自动评估突出了最新机器翻译系统的一些当前限制。它还显示了自动指标，即CHRF，BLEU和COMET，如何在解释性和准确性方面进行补充以减轻自己的限制。

作为对隐喻分析的贡献，我们介绍了一项基于统计的基于数据的研究，并对长期存在的猜想和对隐喻系统特征的有史以来的经验探索进行了经验分析。相反，这也使隐喻理论可作为含义出现的基础，可以定量探索并集成到NLP的框架中。

本文重点介绍了重叠的语音和性别检测，以研究法国视听媒体中男女之间的互动（性别平等监测项目）。在这种应用程序上下文中，我们需要根据说话者的性别自动划分语音信号，并确定至少有两个说话者同时讲话。我们建议使用WAVLM模型，该模型具有在大量语音数据上进行预训练的优点，以构建重叠的语音检测（OSD）和性别检测（GD）系统。在这项研究中，我们使用两个不同的语料库。 Dihard III语料库非常适合OSD任务，但缺乏性别信息。盟友语料库符合项目申请上下文。我们最好的OSD系统是具有WAVLM预训练功能作为输入的时间卷积网络（TCN），该功能达到了Dihard上最先进的F1得分性能。神经GD在法国广播新闻盟友数据的性别平衡子集上接受了WAVLM输入的培训，并获得了97.9％的准确性。这项工作为人类科学研究人员开辟了有关法国媒体中男女表示差异的新观点。