Are extralinguistic signals such as image pixels crucial for inducing constituency grammars? While past work has shown substantial gains from multimodal cues, we investigate whether such gains persist in the presence of rich information from large language models (LLMs). We find that our approach, LLM-based C-PCFG (LC-PCFG), outperforms previous multi-modal methods on the task of unsupervised constituency parsing, achieving state-of-the-art performance on a variety of datasets. Moreover, LC-PCFG results in an over 50% reduction in parameter count, and speedups in training time of 1.7x for image-aided models and more than 5x for video-aided models, respectively. These results challenge the notion that extralinguistic signals such as image pixels are needed for unsupervised grammar induction, and point to the need for better text-only baselines in evaluating the need of multi-modality for the task.
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As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.
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As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as 'Constitutional AI'. The process involves both a supervised learning and a reinforcement learning phase. In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses. In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels.
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“感应头”是注意力头,它实现了一种简单的算法来完成令牌序列,例如[a] [b] ... [a] - > [b]。在这项工作中,我们提供了一个假设的初步和间接证据,即诱导头可能构成大型大型变压器模型中所有“文本学习”中大多数的机制(即减少在增加代币指数时损失的损失)。我们发现,诱导头在与秘密学习能力突然急剧上的急剧上升的位置完全相同,这是训练损失的颠簸。我们提出了六种互补的证据,认为诱导头可能是任何大小的变压器模型中一般性内部学习的机理来源。对于仅关注的小型模型,我们提供了有力的因果证据。对于具有MLP的较大模型,我们提供相关证据。
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神经网络经常将许多无关的概念包装到一个神经元中 - 一种令人困惑的现象被称为“多疾病”,这使解释性更具挑战性。本文提供了一个玩具模型,可以完全理解多义,这是由于模型在“叠加”中存储其他稀疏特征的结果。我们证明了相变的存在,与均匀多型的几何形状的令人惊讶的联系以及与对抗性例子联系的证据。我们还讨论了对机械解释性的潜在影响。
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我们研究语言模型是否可以评估自己主张的有效性,并预测他们能够正确回答的问题。我们首先表明,当以正确的格式提供时,较大的模型在多样化的多项选择和True/False问题上进行了很好的校准。因此,我们可以通过要求模型首先提出答案,然后评估其答案正确的概率“ p(true)”来对开放式采样任务进行自我评估。我们发现在各种任务中,P(true)的表现,校准和缩放令人鼓舞。当我们允许模型考虑自己的许多样本之前,在预测一种特定可能性的有效性之前,自我评估的性能进一步改善。接下来,我们研究是否可以培训模型来预测“ P(ik)”,即“我知道”问题的概率,而无需参考任何特定提出的答案。模型在预测P(IK)方面表现良好,并且在跨任务中部分概括,尽管它们在新任务上的P(IK)校准方面遇到了困难。预测的p(IK)概率在存在相关的原始材料的情况下以及对数学单词问题解决方案的提示也适当增加。我们希望这些观察结果为培训更诚实的模型提供了基础,并研究了诚实对模型模仿人类写作以外的其他目标培训的案例的普遍性。
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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。
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鉴于大型语言模型的广泛能力,应该有可能朝着一般的文本的助手工作,这些助手与人类价值一致,这意味着它是有帮助,诚实的和无害的。在此方向上的初始遗传,我们研究简单的基线技术和评估,例如提示。我们发现,从模型规模增加适度的干预措施的好处,概括为各种对准评估,并不会损害大型模型的性能。接下来,我们调查与对齐,比较仿制,二进制歧视和排名偏好建模相关的几个培训目标的缩放趋势。我们发现排名优先级模型比模仿学习更好地表现得多,并且通常以模型大小更有利地缩放。相比之下,二进制歧视通常与模仿学习非常类似地执行和缩放。最后,我们研究了一种“偏好模型预训练阶段的培训阶段,其目的是在对人偏好的芬明时提高样本效率。
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超材料是复合材料,具有工程化几何微观和中间结构,可以导致罕见的物理性质,如负泊松的比例或超低剪切电阻。周期性超材料由重复单元 - 细胞组成,并且这些单元电池内的几何图案影响弹性或声波和控制分散的传播。在这项工作中,我们开发了一种新的可解释,多分辨率的机器学习框架,用于在揭示其动态特性的材料的单元单元中查找模式。具体而言,我们提出了两个新的超材料的新可解释表示,称为形状频率特征和单元 - 单元格模板。使用这些要素类构建的机器学习模型可以准确地预测动态材料属性。这些特征表示(特别是单个单元格模板)具有有用的属性:它们可以在更高分辨率的设计上运行。通过学习可以通过形状频率特征或单元 - 单元模板可靠地传送到更精细的分辨率设计空间的关键粗略尺度模式,我们几乎可以自由地设计单元单元的精细分辨率特征而不改变粗略级别物理。通过这种多分辨率方法,我们能够设计具有目标频率范围的材料,其中允许或不允许波传播(频率带盖)。我们的方法产生了重大好处:(1)与材料科学的典型机器学习方法不同,我们的模型是可解释的,(2)我们的方法利用多分辨率属性,(3)我们的方法提供了设计灵活性。
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This paper utilizes an anomaly detection algorithm to check if underwater gliders are operating normally in the unknown ocean environment. Glider pilots can be warned of the detected glider anomaly in real time, thus taking over the glider appropriately and avoiding further damage to the glider. The adopted algorithm is validated by two valuable sets of data in real glider deployments, the University of South Florida (USF) glider Stella and the Skidaway Institute of Oceanography (SkIO) glider Angus.
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