Large language models (LLMs) can acquire strong code-generation capabilities through few-shot learning. In contrast, supervised fine-tuning is still needed for smaller models to achieve good performance. Such fine-tuning demands a large number of task-specific NL-code pairs, which are expensive to obtain. In this paper, we attempt to transfer the code generation ability of an LLM to a smaller model with the aid of weakly-supervised data. More specifically, we propose explicit knowledge transfer (EKT), which uses the few-shot capabilities of a teacher LLM to create NL-code pairs that we then filter for correctness and fine-tune the student on. We evaluate EKT on the task of generating code solutions to math word problems from the GSM8k dataset. We find that EKT not only yields better performance than training with expert iteration, but also outperforms knowledge distillation, another form of knowledge transfer. A GPT-Neo 1.3B model trained using EKT with a GPT-J teacher achieves a 12.4% pass@100 on GSM8k, while the same student and teacher trained with knowledge distillation yield only a 3.7% pass@100. We also show that it is possible for a student model to outperform the teacher using EKT.

Language models (LMs) have demonstrated remarkable performance on downstream tasks, using in-context exemplars or human instructions. Recent works have shown that chain-of-thought (CoT) prompting can elicit models to solve complex reasoning tasks, step-by-step. However, the efficacy of prompt-based CoT methods is restricted to very large LMs such as GPT-3 (175B), thus limiting deployability. In this paper, we revisit the fine-tuning approach to enable complex reasoning in smaller LMs, optimized to efficiently perform a specific task. We propose Fine-tune-CoT, a method that leverages the capabilities of very large LMs to generate reasoning samples and teach smaller models via fine-tuning. We evaluate our method on publicly available LMs across a wide range of complex tasks and model sizes. We find that Fine-tune-CoT enables substantial reasoning capability in small models, whereas previous prompt-based baselines exhibit near-random performance. Student models can even outperform the teacher in some tasks while reducing model size requirements by several orders of magnitude. We conduct extensive ablations and sample studies to understand the reasoning capabilities of student models. We also identify several important nuances that have been overlooked in concurrent fine-tuning works on CoT and address them in our analysis.

Step-by-step reasoning approaches like chain-of-thought (CoT) have proved to be a very effective technique to induce reasoning capabilities in large language models. However, the success of the CoT approach depends primarily on model size, and often billion parameter-scale models are needed to get CoT to work. In this paper, we propose a knowledge distillation approach, that leverages the step-by-step CoT reasoning capabilities of larger models and distils these reasoning abilities into smaller models. Our approach Decompositional Distillation learns a semantic decomposition of the original problem into a sequence of subproblems and uses it to train two models: a) a problem decomposer that learns to decompose the complex reasoning problem into a sequence of simpler sub-problems and b) a problem solver that uses the intermediate subproblems to solve the overall problem. On a multi-step math word problem dataset (GSM8K), we boost the performance of GPT-2 variants up to 35% when distilled with our approach compared to CoT. We show that using our approach, it is possible to train a GPT-2-large model (775M) that can outperform a 10X larger GPT-3 (6B) model trained using CoT reasoning. Finally, we also demonstrate that our approach of problem decomposition can also be used as an alternative to CoT prompting, which boosts the GPT-3 performance by 40% compared to CoT prompts.

这项工作表明了如何以编程难题的形式使用大规模语言模型（LMS）与经过验证的解决方案合成编程问题，然后可以将其用于微调相同的模型，从而提高其性能。这项工作以最近的两项发展为基础。首先，LMS在非平凡的推理和算法实施中取得了突破，生成可以解决某些中级竞争性编程问题的代码。但是，培训代码LMS涉及策划的一组自然语言问题描述以及源代码测试和解决方案，这些测试和解决方案的大小有限。其次，引入了一种新的编程挑战格式，称为编程难题，该格式不需要自然语言描述，并通过源代码测试直接指定。在这项工作中，我们展示了如何使用Python解释器验证的合成编程难题和解决方案，可用于改善从P3求解测试难题的性能，P3是一套Python公共基准的Python编程难题。此外，我们发布了由Codex模型生成的100万个难题和解决方案的数据集，我们证明可以通过微调改善较小的模型。

Given the success with in-context learning of large pre-trained language models, we introduce in-context learning distillation to transfer in-context few-shot learning ability from large models to smaller models. We propose to combine in-context learning objectives with language modeling objectives to distill both the ability to read in-context examples and task knowledge to the smaller models. We perform in-context learning distillation under two different few-shot learning paradigms: Meta In-context Tuning (Meta-ICT) and Multitask In-context Tuning (Multitask-ICT). Multitask-ICT performs better on multitask few-shot learning but also requires more computation than Meta-ICT. Our method shows consistent improvements for both Meta-ICT and Multitask-ICT on two benchmarks: LAMA and CrossFit. Our extensive experiments and analysis reveal that in-context learning objectives and language modeling objectives are complementary under the Multitask-ICT paradigm. In-context learning objectives achieve the best performance when combined with language modeling objectives.

Chain of thought prompting successfully improves the reasoning capabilities of large language models, achieving state of the art results on a range of datasets. However, these reasoning capabilities only appear to emerge in models with a size of over 100 billion parameters. In this paper, we explore the transfer of such reasoning capabilities to models with less than 100 billion parameters via knowledge distillation. Specifically, we finetune a student model on the chain of thought outputs generated by a larger teacher model. Our experiments show that the proposed method improves task performance across arithmetic, commonsense and symbolic reasoning datasets. For example, the accuracy of T5 XXL on GSM8K improves from 8.11% to 21.99% when finetuned on PaLM-540B generated chains of thought.

专家层（MOES）的混合物通过条件计算实现语言模型的高效缩放。本文提出了一个详细的实证研究，自回归鞋语言模型与广泛的设置中的密集模型相比：在域外语言建模，零和少量射击和全部微调。除了微调外，我们发现Moes基本上更加计算效率。在更适度的培训预算下，MOES可以使用$ \ SIM值4倍的计算，符合密集模型的性能。该差距在比例下变窄，但我们最大的MOE模型（1.1T参数）始终如一地优于计算等效的密集模型（6.7b参数）。总体而言，这种表现差距在任务和域中有很大差异，表明MOE和密集模型以不值得研究的方式概括不同的方式。我们使我们的代码和模型公开可用于研究使用。

The common practice for training commonsense models has gone from-human-to-corpus-to-machine: humans author commonsense knowledge graphs in order to train commonsense models. In this work, we investigate an alternative, from-machine-to-corpus-to-machine: general language models author these commonsense knowledge graphs to train commonsense models. Our study leads to a new framework, Symbolic Knowledge Distillation. As with prior art in Knowledge Distillation (Hinton et al., 2015), our approach uses larger models to teach smaller models. A key difference is that we distill knowledge symbolically-as text-in addition to the neural model. We also distill only one aspect-the commonsense of a general language model teacher, allowing the student to be a different type, a commonsense model. Altogether, we show that careful prompt engineering and a separately trained critic model allow us to selectively distill high-quality causal commonsense from GPT-3, a general language model. Empirical results demonstrate that, for the first time, a human-authored commonsense knowledge graph is surpassed by our automatically distilled variant in all three criteria: quantity, quality, and diversity. In addition, it results in a neural commonsense model that surpasses the teacher model's commonsense capabilities despite its 100x smaller size. We apply this to the ATOMIC resource, and share our new symbolic knowledge graph and commonsense models.

半监督学习（SSL）在许多应用领域中已经取得了成功，但这种成功经常涉及任务特定的未标记数据的可用性。知识蒸馏（KD）能够有效地优化紧凑的神经网络，当通过新鲜任务特定的未标记数据蒸馏昂贵的网络时，实现了最佳结果。但是，任务特定的未标记数据可能具有挑战性，特别是对于NLP。我们调查使用生成模型在合成未标记数据中的使用，并呈现一个名为“生成，注释和学习（GAL）”的简单和一般框架。语言模型（LM）用于扫描域中的未标记数据。然后，分类器用于注释这样的数据。最后，综合生成和注释的数据用于推进SSL，KD和NLP和表格任务的几次拍摄学习。为了获得强大的任务特定的LM，我们要么微调来自特定任务的输入的大LM，或者提示具有少数输入示例的大型LM，并且有条件地生成更明显的示例。它还为胶水排行榜上的6层变压器产生了一种新的最先进的。最后，使用GAL的自我训练从UCI存储库的四个表格任务上提供大的收益。

已经证明了对比学习适合学习句子嵌入，可以显着提高语义文本相似性（STS）任务。最近，大型对比学习模型，例如句子T5倾向于学到更强大的句子嵌入。虽然有效，但由于计算资源或时间成本限制，这种大型型号很难在线服务。为了解决这个问题，通常采用知识蒸馏（KD），这可以将大型“教师”模型压缩成一个小的“学生”模型，但通常会遭受一些性能损失。在这里，我们提出了一个增强的KD框架，称为蒸馏 - 对比度（迪斯科）。所提出的迪斯科框架首先利用KD将大句子嵌入模型的能力转移到大型未标记数据的小学生模型，然后在标记的训练数据上具有对比学习的学生模型。对于迪斯科舞厅的KD进程，我们进一步提出了对比的知识蒸馏（CKD），以增强教师模型培训，KD和学生模型的一致性，这可能会提高迅速学习的表现。 7 STS基准测试的广泛实验表明，使用所提出的迪斯科和CKD培训的学生模型很少或甚至没有性能损失，并且始终如一地优于相同参数大小的相应对应物。令人惊讶的是，我们的110米学生模型甚至可以优于最新的最新（SOTA）模型，即句子T5（11B），只有1％的参数。

程序合成或代码生成旨在生成满足问题规范的程序。使用大规模预处理的语言模型（LMS）的最新方法显示出令人鼓舞的结果，但它们有一些关键的局限性。特别是，他们经常遵循标准监督的微调程序，仅从对自然语言问题描述和基础真相计划对培训代码生成模型。这种范式在很大程度上忽略了问题规范中的一些重要但潜在的信号，例如单位测试，因此在求解复杂的看不见的编码任务时通常会导致性能差。为了解决这些局限性，我们提出了“ Coderl”，这是通过验证的LMS和深入强化学习（RL）实现程序合成任务的新框架。具体而言，在培训期间，我们将代码生成的LM视为参与者网络，并引入批评网络，该网络经过培训，以预测生成的程序的功能正确性，并为演员提供密集的反馈信号。在推理期间，我们引入了一种新一代程序，具有关键的抽样策略，该过程允许模型根据示例单位测试和评论家分数的反馈自动重新生成程序。对于模型骨架，我们扩展了Codet5的编码器架构，具有增强的学习目标，更大的模型大小和更好的预处理数据。我们的方法不仅在具有挑战性的应用程序基准上实现了新的SOTA结果，而且还显示出强大的零弹性传输能力，并在简单的MBPP基准上具有新的SOTA结果。

GPT-3等大型语言模型是优秀的几次学习者，允许他们通过自然文本提示来控制。最近的研究报告称，基于及时的直接分类消除了对微调的需求，但缺乏数据和推理可扩展性。本文提出了一种新的数据增强技术，利用大规模语言模型来生成来自真实样本的混合的现实文本样本。我们还建议利用语言模型预测的软标签，从大规模语言模型中有效地蒸馏知识并同时创建文本扰动。我们对各种分类任务进行数据增强实验，并显示我们的方法非常优于现有的文本增强方法。消融研究和定性分析为我们的方法提供了更多的见解。

我们提出了Pangu-Coder，这是一种仅预读的解码器语言模型，该模型采用pangu-alpha架构进行文本到代码生成，即给定自然语言问题描述的编程语言解决方案的合成。我们使用两阶段策略训练Pangu-Coder：第一阶段采用因果语言建模（CLM）来预先培训原始编程语言数据，而第二阶段则使用因果语言建模和掩盖语言建模（MLM）的组合培训目标，专注于文本到代码生成的下游任务，并培训松散的自然语言程序定义和代码功能。最后，我们讨论了pangu-coder-ft，该pander the是通过竞争性编程问题和代码与持续集成测试的结合进行了微调的。我们评估了pangu-coder，重点是它是否生成功能上正确的程序，并证明它在参加较小的上下文窗口和较少的数据培训的同时，它比诸如Codex之类的类似大小的模型（例如Codex）实现等效性或更好的性能。

Knowledge distillation (KD) has been widely used for model compression and knowledge transfer. Typically, a big teacher model trained on sufficient data transfers knowledge to a small student model. However, despite the success of KD, little effort has been made to study whether KD leaks the training data of the teacher model. In this paper, we experimentally reveal that KD suffers from the risk of privacy leakage. To alleviate this issue, we propose a novel knowledge distillation method, swing distillation, which can effectively protect the private information of the teacher model from flowing to the student model. In our framework, the temperature coefficient is dynamically and adaptively adjusted according to the degree of private information contained in the data, rather than a predefined constant hyperparameter. It assigns different temperatures to tokens according to the likelihood that a token in a position contains private information. In addition, we inject noise into soft targets provided to the student model, in order to avoid unshielded knowledge transfer. Experiments on multiple datasets and tasks demonstrate that the proposed swing distillation can significantly reduce (by over 80% in terms of canary exposure) the risk of privacy leakage in comparison to KD with competitive or better performance. Furthermore, swing distillation is robust against the increasing privacy budget.

基于变压器的语言模型应用于自然语言处理的广泛应用程序。但是，它们效率低，难以部署。近年来，已经提出了许多压缩算法来提高目标硬件上大型变压器的模型的实现效率。在这项工作中，我们通过整合体重修剪和模型蒸馏来提出一种训练稀疏预训练的变压器语言模型的新方法。这些稀疏的预训练型号可用于在维护稀疏模式的同时传输广泛的任务。我们展示了我们有三个已知的架构的方法，以创建稀疏的预训练伯特基，BERT-MAT​​RY和DISTOLBERT。我们展示了压缩稀疏的预训练模型如何培训他们的知识，以最小的精度损失将他们的知识转移到五种不同的下游自然语言任务。此外，我们展示了如何使用量化感知培训进一步将稀疏模型的重量压缩为8位精度。例如，在SQUAdv1.1上使用我们稀疏预训练的BERT频率，并量化为8位，我们为编码器达到40美元的压缩比，而不是1 \％$精度损失。据我们所知，我们的结果表明Bert-Base，Bert-Light和Distilbert的最佳压缩至准确率。

缺乏标记数据是关系提取的主要障碍。通过将未标记的样本作为额外培训数据注释，已经证明，半监督联系提取（SSRE）已被证明是一个有希望的方法。沿着这条线几乎所有先前的研究采用多种模型来使注释通过从这些模型中获取交叉路口集的预测结果来更加可靠。然而，差异集包含有关未标记数据的丰富信息，并通过事先研究忽略了忽视。在本文中，我们建议不仅从共识中学习，而且还要学习SSRE中不同模型之间的分歧。为此，我们开发了一种简单且一般的多教师蒸馏（MTD）框架，可以轻松集成到任何现有的SSRE方法中。具体来说，我们首先让教师对应多个模型，并在SSRE方法中选择最后一次迭代的交叉点集中的样本，以便像往常一样增加标记的数据。然后，我们将类分布转移为差异设置为软标签以指导学生。我们最后使用训练有素的学生模型进行预测。两个公共数据集上的实验结果表明，我们的框架显着促进了基础SSRE方法的性能，具有相当低的计算成本。

上下文学习是最近的自然语言理解的范例，其中大型预先接受的语言模型（LM）观察测试实例和一些训练示例作为其输入，并直接对输出进行解码，而不会对其参数进行任何更新。但是，表现已被证明强烈依赖于所选培训示例（称为提示）。在这项工作中，我们提出了一种有效的方法，用于使用注释的数据和LM检索内心学习的提示。给定输入输出对，我们估计给出输入和候选训练示例的输出的概率作为提示，以及基于这种概率的正面或负标记训练示例。然后，我们从该数据中培训一个有效的密集鼠尾，用于检索训练示例作为测试时间的提示。我们在三个序列到序列任务中评估我们的方法，其中语言话语映射到意义表示，并发现它基本上优于前面的工作和电路板的多个基线。

Knowledge distillation aims at transferring knowledge acquired in one model (a teacher) to another model (a student) that is typically smaller. Previous approaches can be expressed as a form of training the student to mimic output activations of individual data examples represented by the teacher. We introduce a novel approach, dubbed relational knowledge distillation (RKD), that transfers mutual relations of data examples instead. For concrete realizations of RKD, we propose distance-wise and angle-wise distillation losses that penalize structural differences in relations. Experiments conducted on different tasks show that the proposed method improves educated student models with a significant margin. In particular for metric learning, it allows students to outperform their teachers' performance, achieving the state of the arts on standard benchmark datasets.

Free-text rationales (FTRs) follow how humans communicate by explaining reasoning processes via natural language. A number of recent works have studied how to improve language model (LM) generalization by using FTRs to teach LMs the correct reasoning processes behind correct task outputs. These prior works aim to learn from FTRs by appending them to the LM input or target output, but this may introduce an input distribution shift or conflict with the task objective, respectively. We propose KNIFE, which distills FTR knowledge from an FTR-augmented teacher LM (takes both task input and FTR) to a student LM (takes only task input), which is used for inference. Crucially, the teacher LM's forward computation has a bottleneck stage in which all of its FTR states are masked out, which pushes knowledge from the FTR states into the task input/output states. Then, FTR knowledge is distilled to the student LM by training its task input/output states to align with the teacher LM's. On two question answering datasets, we show that KNIFE significantly outperforms existing FTR learning methods, in both fully-supervised and low-resource settings.

很少有学习模型学习人类注释有限，而这种学习范式在各种任务中证明了实用性数据使该模型无法充分探索语义信息。为了解决这个问题，我们将知识蒸馏引入了几个弹出的对象检测学习范式。我们进一步进行了激励实验，该实验表明，在知识蒸馏的过程中，教师模型的经验误差将少数拍物对象检测模型的预测性能（作为学生）退化。为了了解这种现象背后的原因，我们从因果理论的角度重新审视了几个对象检测任务上知识蒸馏的学习范式，并因此发展了一个结构性因果模型。遵循理论指导，我们建议使用基于后门调整的知识蒸馏方法，用于少数拍物检测任务，即Disentangle和Remerge（D＆R），以对相应的结构性因果模型进行有条件的因果干预。从理论上讲，我们为后门标准提供了扩展的定义，即一般后门路径，可以在特定情况下扩展后门标准的理论应用边界。从经验上讲，多个基准数据集上的实验表明，D＆R可以在几个射击对象检测中产生显着的性能提升。