Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs). CoT explicitly encourages the LLM to generate intermediate rationales for solving a problem, by providing a series of reasoning steps in the demonstrations. Despite its success, there is still little understanding of what makes CoT prompting effective and which aspects of the demonstrated reasoning steps contribute to its performance. In this paper, we show that CoT reasoning is possible even with invalid demonstrations - prompting with invalid reasoning steps can achieve over 80-90% of the performance obtained using CoT under various metrics, while still generating coherent lines of reasoning during inference. Further experiments show that other aspects of the rationales, such as being relevant to the query and correctly ordering the reasoning steps, are much more important for effective CoT reasoning. Overall, these findings both deepen our understanding of CoT prompting, and open up new questions regarding LLMs' capability to learn to reason in context.

Although large language models can be prompted for both zero- and few-shot learning, performance drops significantly when no demonstrations are available. In this paper, we introduce Z-ICL, a new zero-shot method that closes the gap by constructing pseudo-demonstrations for a given test input using a raw text corpus. Concretely, pseudo-demonstrations are constructed by (1) finding the nearest neighbors to the test input from the corpus and pairing them with random task labels, and (2) applying a set of techniques to reduce the amount of direct copying the model does from the resulting demonstrations. Evaluation on nine classification datasets shows that Z-ICL outperforms previous zero-shot methods by a significant margin, and is on par with in-context learning with labeled training data in the few-shot setting. Overall, Z-ICL provides a significantly higher estimate of the zero-shot performance levels of a model, and supports future efforts to develop better pseudo-demonstrations that further improve zero-shot results.

Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases. We introduce NPM, the first nonparametric masked language model that replaces this softmax with a nonparametric distribution over every phrase in a reference corpus. We show that NPM can be efficiently trained with a contrastive objective and an in-batch approximation to full corpus retrieval. Zero-shot evaluation on 9 closed-set tasks and 7 open-set tasks demonstrates that NPM outperforms significantly larger parametric models, either with or without a retrieve-and-generate approach. It is particularly better on dealing with rare patterns (word senses or facts), and predicting rare or nearly unseen words (e.g., non-Latin script). We release the model and code at github.com/facebookresearch/NPM.

Information seeking users often pose questions with false presuppositions, especially when asking about unfamiliar topics. Most existing question answering (QA) datasets, in contrast, assume all questions have well defined answers. We introduce CREPE, a QA dataset containing a natural distribution of presupposition failures from online information-seeking forums. We find that 25% of questions contain false presuppositions, and provide annotations for these presuppositions and their corrections. Through extensive baseline experiments, we show that adaptations of existing open-domain QA models can find presuppositions moderately well, but struggle when predicting whether a presupposition is factually correct. This is in large part due to difficulty in retrieving relevant evidence passages from a large text corpus. CREPE provides a benchmark to study question answering in the wild, and our analyses provide avenues for future work in better modeling and further studying the task.

在寻求信息的对话中，用户与代理商进行对话，以提出一系列通常可以不足或过度指定的问题。理想的代理商首先将通过搜索其基本知识来源，然后与用户进行适当互动以解决它，从而确定他们处于这种情况。但是，大多数现有研究都无法或人为地纳入此类代理端计划。在这项工作中，我们介绍了Inscit（发音为Insight），这是一种用于与混合互动相互作用的信息寻求对话的数据集。它包含从805个人类对话中进行的4.7k用户代理转弯，代理商对Wikipedia进行搜索，并要求澄清或提供相关信息以解决用户查询。我们定义了两个子任务，即证据通过识别和响应产生，以及一种新的人类评估协议来评估模型绩效。我们根据对话知识识别和开放域问题的最新模型报告了两个强大的基线的结果。这两种模型都显着不足，并且没有产生连贯和信息丰富的反应，这表明未来的研究有足够的改进空间。

Fine Tuning Target Tasks的连续提示最近被出现为完整模型微调的紧凑替代方案。这些有前途的结果的动机，我们调查了提取离散（文本）解释的可行性，持续提示忠于他们解决的问题。在实践中，我们在通过连续提示和最近的邻离分立投影解决的任务之间的“任性”行为：我们可以找到解决任务的连续提示，同时投射到任意文本（例如，不同甚至a的定义矛盾的任务），而在最佳连续提示的非常小（2％）的边缘内，对于任务相同的相同尺寸。我们提供这种奇怪和令人惊讶的行为背后的直觉，以及广泛的实证分析量化各种参数的效果。例如，对于更大的模型大小，我们观察到更高的任性，即，我们可以发现提示更紧密地映射到任何随意的任意文本，精度较小。这些调查结果与忠实地解释模型和任务持续提示及其概括的难度有关的重要意义，为提示语言模型的未来进展提供指导。

Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dualencoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system greatly by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks. 1 * Equal contribution 1 The code and trained models have been released at https://github.com/facebookresearch/DPR.

基于分数的生成模型（SGM）是生成假图像的最新突破。已知SGM可以超越其他生成模型，例如生成对抗网络（GAN）和变异自动编码器（VAE）。在这项工作中，我们受到了他们的巨大成功的启发，我们将它们完全自定义以生成伪造的表格数据。特别是，我们对过度采样较小的课程感兴趣，因为不平衡的课程经常导致次优训练成果。据我们所知，我们是第一个提出基于得分的表格数据超采样方法的人。首先，我们必须重新设计自己的分数网络，因为我们必须处理表格数据。其次，我们为我们的生成方法提出了两个选项：前者等同于表格数据的样式传输，后者使用SGMS的标准生成策略。最后，我们定义了一种微调方法，该方法进一步提高了过度采样质量。在我们使用6个数据集和10个基线的实验中，我们的方法在所有情况下都优于其他过采样方法。

The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in Multi-access Edge Computing (MEC). Diverse user behaviors call for personalized services with heterogeneous Machine Learning (ML) models on different devices. Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC. Introducing knowledge distillation into FMTL can simultaneously enable efficient communication and model heterogeneity among clients, whereas existing methods rely on a public dataset, which is impractical in reality. To tackle this dilemma, Federated MultI-task Distillation for Multi-access Edge CompuTing (FedICT) is proposed. FedICT direct local-global knowledge aloof during bi-directional distillation processes between clients and the server, aiming to enable multi-task clients while alleviating client drift derived from divergent optimization directions of client-side local models. Specifically, FedICT includes Federated Prior Knowledge Distillation (FPKD) and Local Knowledge Adjustment (LKA). FPKD is proposed to reinforce the clients' fitting of local data by introducing prior knowledge of local data distributions. Moreover, LKA is proposed to correct the distillation loss of the server, making the transferred local knowledge better match the generalized representation. Experiments on three datasets show that FedICT significantly outperforms all compared benchmarks in various data heterogeneous and model architecture settings, achieving improved accuracy with less than 1.2% training communication overhead compared with FedAvg and no more than 75% training communication round compared with FedGKT.

In this paper we study the smooth strongly convex minimization problem $\min_{x}\min_y f(x,y)$. The existing optimal first-order methods require $\mathcal{O}(\sqrt{\max\{\kappa_x,\kappa_y\}} \log 1/\epsilon)$ of computations of both $\nabla_x f(x,y)$ and $\nabla_y f(x,y)$, where $\kappa_x$ and $\kappa_y$ are condition numbers with respect to variable blocks $x$ and $y$. We propose a new algorithm that only requires $\mathcal{O}(\sqrt{\kappa_x} \log 1/\epsilon)$ of computations of $\nabla_x f(x,y)$ and $\mathcal{O}(\sqrt{\kappa_y} \log 1/\epsilon)$ computations of $\nabla_y f(x,y)$. In some applications $\kappa_x \gg \kappa_y$, and computation of $\nabla_y f(x,y)$ is significantly cheaper than computation of $\nabla_x f(x,y)$. In this case, our algorithm substantially outperforms the existing state-of-the-art methods.