Current image generation models struggle to reliably produce well-formed visual text. In this paper, we investigate a key contributing factor: popular text-to-image models lack character-level input features, making it much harder to predict a word's visual makeup as a series of glyphs. To quantify the extent of this effect, we conduct a series of controlled experiments comparing character-aware vs. character-blind text encoders. In the text-only domain, we find that character-aware models provide large gains on a novel spelling task (WikiSpell). Transferring these learnings onto the visual domain, we train a suite of image generation models, and show that character-aware variants outperform their character-blind counterparts across a range of novel text rendering tasks (our DrawText benchmark). Our models set a much higher state-of-the-art on visual spelling, with 30+ point accuracy gains over competitors on rare words, despite training on far fewer examples.

The attention mechanism is considered the backbone of the widely-used Transformer architecture. It contextualizes the input by computing input-specific attention matrices. We find that this mechanism, while powerful and elegant, is not as important as typically thought for pretrained language models. We introduce PAPA, a new probing method that replaces the input-dependent attention matrices with constant ones -- the average attention weights over multiple inputs. We use PAPA to analyze several established pretrained Transformers on six downstream tasks. We find that without any input-dependent attention, all models achieve competitive performance -- an average relative drop of only 8% from the probing baseline. Further, little or no performance drop is observed when replacing half of the input-dependent attention matrices with constant (input-independent) ones. Interestingly, we show that better-performing models lose more from applying our method than weaker models, suggesting that the utilization of the input-dependent attention mechanism might be a factor in their success. Our results motivate research on simpler alternatives to input-dependent attention, as well as on methods for better utilization of this mechanism in the Transformer architecture.

大型语言模型（例如GPT-3（Brown等，2020）可以执行任意任务，而无需在仅使用少数标签示例的提示之后进行微调。可以将任意任务重新构成自然语言提示，并且可以要求语言模型生成完成，并以称为基于及时的学习的范式间接执行该任务。迄今为止，主要针对单向语言模型证明了新兴迅速的学习能力。但是，预先培训的双向语言模型（例如蒙版语言建模）为转移学习提供了更强大的学习表示。这激发了促使双向模型的可能性，但是它们的预训练目标使它们与现有的提示范式不相容。我们提出SAP（顺序自动回旋提示），该技术可以使双向模型提示。利用机器翻译任务作为案例研究，我们提示了带有SAP的双向MT5模型（Xue等，2021），并演示其少量拍摄和零照片的翻译优于GPT-3等单向模型的几个单拍翻译和XGLM（Lin等，2021），尽管MT5的参数减少了约50％。我们进一步表明SAP对问题的回答和摘要有效。我们的结果首次表明基于及时的学习是更广泛的语言模型的新兴属性，而不仅仅是单向模型。

参数效率的方法能够使用单个冷冻的预训练的大语言模型（LLM）来通过学习特定于任务的软提示来执行许多任务，从而在串联到输入文本时调节模型行为。但是，这些学习的提示与给定的冷冻模型紧密耦合 - 如果模型已更新，则需要获得相应的新提示。在这项工作中，我们提出并调查了几种“提示回收”的方法，其中将在源模型上进行了及时培训以与新目标模型一起使用。我们的方法不依赖于目标模型的有监督的提示，特定于任务的数据或培训更新，这与从头开始的目标模型重新调整提示一样昂贵。我们表明，模型之间的回收是可能的（我们的最佳设置能够成功回收$ 88.9 \％的提示，从而产生一个提示，即表现出色的基线），但是剩下的大量性能净空，需要改进的回收技术。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

我们提供了从文本到文本变换器（T5）的第一次探索句子嵌入式。句子嵌入式广泛适用于语言处理任务。虽然T5在作为序列到序列映射问题的语言任务上实现令人印象深刻的性能，但目前尚不清楚如何从编码器解码器模型生成陈列嵌入的句子。我们调查三种方法提取T5句子嵌入方法：两个仅利用T5编码器，一个使用全T5编码器解码器模型。为了支持我们的调查，我们建立了一个新的句子代表转移基准，SentGlue，它将Senteval Toolkit扩展到粘合基准的九个任务。我们的编码器的型号优于Senteval和SentGlue传输任务的句子 - BERT和SIMCSE句子嵌入，包括语义文本相似性（STS）。发现从数百万到数十亿参数的缩放T5产生一致的进一步改进。最后，我们的编码器 - 解码器方法在使用句子嵌入时在STS上实现了新的最先进的。我们的模型在https://tfhub.dev/google/collections/sentence-t5/1发布。

In this work, we explore "prompt tuning," a simple yet effective mechanism for learning "soft prompts" to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from any number of labeled examples. Our end-to-end learned approach outperforms GPT-3's few-shot learning by a large margin. More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method "closes the gap" and matches the strong performance of model tuning (where all model weights are tuned). This finding is especially relevant because large models are costly to share and serve and the ability to reuse one frozen model for multiple downstream tasks can ease this burden. Our method can be seen as a simplification of the recently proposed "prefix tuning" of Li and Liang (2021) and we provide a comparison to this and other similar approaches. Finally, we show that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer and enables efficient "prompt ensembling." * Work done as a Google AI Resident.

Effective management of public shared spaces such as car parking space, is one challenging transformational aspect for many cities, especially in the developing World. By leveraging sensing technologies, cloud computing, and Artificial Intelligence, Cities are increasingly being managed smartly. Smart Cities not only bring convenience to City dwellers, but also improve their quality of life as advocated for by United Nations in the 2030 Sustainable Development Goal on Sustainable Cities and Communities. Through integration of Internet of Things and Cloud Computing, this paper presents a successful proof-of-concept implementation of a framework for managing public car parking spaces. Reservation of parking slots is done through a cloud-hosted application, while access to and out of the parking slot is enabled through Radio Frequency Identification (RFID) technology which in real-time, accordingly triggers update of the parking slot availability in the cloud-hosted database. This framework could bring considerable convenience to City dwellers since motorists only have to drive to a parking space when sure of a vacant parking slot, an important stride towards realization of sustainable smart cities and communities.

Despite recent success in large language model (LLM) reasoning, LLMs still struggle with hierarchical multi-step reasoning like generating complex programs. In these cases, humans often start with a high-level algorithmic design and implement each part gradually. We introduce Parsel, a framework enabling automatic implementation and validation of complex algorithms with code LLMs, based on hierarchical function descriptions in natural language. Parsel can be used across domains requiring hierarchical reasoning, e.g. code synthesis, theorem proving, and robotic planning. We demonstrate Parsel's capabilities by using it to generate complex programs that cannot currently be automatically implemented from one description and backtranslating Python programs in the APPS dataset. Beyond modeling capabilities, Parsel allows problem-solving with high-level algorithmic designs, benefiting both students and professional programmers.

Large "instruction-tuned" language models (finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model. We introduce Self-Instruct, a framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off its own generations. Our pipeline generates instruction, input, and output samples from a language model, then prunes them before using them to finetune the original model. Applying our method to vanilla GPT3, we demonstrate a 33% absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT_001, which is trained with private user data and human annotations. For further evaluation, we curate a set of expert-written instructions for novel tasks, and show through human evaluation that tuning GPT3 with Self-Instruct outperforms using existing public instruction datasets by a large margin, leaving only a 5% absolute gap behind InstructGPT_001. Self-Instruct provides an almost annotation-free method for aligning pre-trained language models with instructions, and we release our large synthetic dataset to facilitate future studies on instruction tuning.