生成的AI是一个新开发的领域，呈指数式蓬勃发展，并具有工作机会。公司正在寻找具有必要技术能力的候选人和建立AI模型的现实经验。此面试问题清单包括描述性答案问题，简短的答案问题以及MCQ，这些问题将为您提供任何生成AI面试的准备。这些问题涵盖了从AI的基础到将复杂算法付诸实践的所有内容。因此，让我们开始从生成的AI面试问题开始！
了解有关生成AI的所有知识，并使用我们的GenaipinnacleProgram成为Genai专家。
这是我们在下一次面试之前必须知道的有关生成AI的问题和答案的全面清单。
答：变压器是Vaswani等人的2017年论文“关注就是您所需要的”中引入的一种神经网络架构。它已成为许多最先进的自然语言处理模型的骨干。
以下是有关变形金刚的关键点：
变形金刚彻底改变了NLP，并继续成为高级AI模型开发的关键组成部分。
答案：注意是一种生成AI和神经网络中的技术，它允许模型在生成输出时专注于特定的输入区域。它使模型能够动态确定序列中每个输入组件的相对重要性，而不是类似地考虑所有输入组件。
自我注意事项也称为注意事项内，使模型能够专注于输入序列中的各个点。它在变压器体系结构中起着至关重要的作用。
它如何工作？
好处：
该技术使模型可以通过同时执行许多注意力流程来参与来自许多表示子空间的数据。
它如何工作？
好处：
该技术使该模型可以在参与另一个信息的同时处理一个序列，并经常在编码器数据系统中使用。
它如何工作？
好处：
因果关注也被称为掩盖的注意力，是一种在自回归模型中使用的技术，可阻止模型专注于将来呈现的令牌。
它如何工作？
好处：
当地注意力如何工作？
当地关注的好处：
这些关注过程具有优势，并且在特定的任务或模型架构中最有效。任务的特殊需求，可用的处理能力以及模型性能和效率之间的预期权衡是影响注意机制选择的因素。
答：变形金刚在许多自然语言处理任务中已取代了重复的神经网络（RNN）体系结构。这是关于如何以及为什么通常认为变压器比RNN更好的解释：
方法：变形金刚并行处理整个序列。
为什么更好：
方法：变形金刚使用自我注意力直接建模所有对代币对之间的关系。
为什么更好：
方法：变压器使用多头注意力，使他们可以同时专注于输入的不同部分。
为什么更好：
方法：变压器使用位置编码来注入序列顺序信息。
为什么更好：
方法：通过增加图层，注意力头或模型维度的数量，可以轻松地扩展变压器体系结构。
为什么更好：
如何：可以针对各种下游任务进行微调进行预训练的变压器模型。
为什么更好：
方法：变形金刚保持短序列和长序列的性能。
为什么更好：
即使变形金刚在许多应用程序中取代了它们，RNN仍然发挥作用。当计算资源稀缺或数据的顺序特征至关重要时，尤其如此。但是，由于其性能和效率提高，变压器现在是大多数大型NLP工作负载的推荐设计。
答案：这些模型是自然语言处理的重大进步，所有模型均基于变压器体系结构。
答案：大型语言模型（LLM）是一种人工智能（AI）程序，可以识别和生成文本以及其他任务。 LLM经过大量数据的培训，因此名称为“大”。 LLM建立在机器学习的基础上；具体而言，一种称为变压器模型的神经网络。
更简单地说，LLM是一项计算机程序，已经提供了足够的实例来识别和理解复杂的数据，例如人类语言。互联网上数千或数百万兆字节的文本用于培训大量LLM。但是，LLM的程序员可以选择采用更精心选择的数据集，因为样本的口径会影响LLMS学习自然语言的成功程度。
基础LLM（大型语言模型）是一种预先训练的模型，该模型在大量多样的文本数据中训练，以理解和生成人类语言。这种预训练使模型可以学习语言的结构，细微差别和模式，但总的来说，而无需针对任何特定的任务或域而量身定制。示例包括GPT-3和GPT-4。
微调的LLM是一种基础LLM，在较小的，特定于任务的数据集上进行了额外的培训，以提高其针对特定应用程序或域的性能。这个微调过程会调整模型的参数，以更好地处理特定任务，例如情感分析，机器翻译或问题答案，从而更有效和准确。
答：许多任务可为LLM训练。它们在生成AI中的用途是其最著名的应用程序之一。例如，公开访问的LLM Chatgpt可能会根据用户的输入产生诗歌，论文和其他文本格式。
任何大型复杂的数据集都可以用于培训LLM，包括编程语言。一些LLM可以帮助程序员编写代码。他们可以根据要求编写功能 - 或以某些代码为起点，他们可以完成编写程序。 LLM也可以使用：
现实世界中LLM的示例包括Chatgpt（来自Openai），Gemini（Google）和Llama（Meta）。 Github的副驾驶是另一个例子，但用于编码而不是自然的人类语言。
答案：LLM的关键特征是他们对不可预测的查询做出反应的能力。传统的计算机程序以其接受的语法或用户的一组输入集接收命令。视频游戏具有有限的按钮；应用程序具有用户可以单击或键入的有限内容集，并且编程语言由精确的if/then语句组成。
另一方面，LLM可以利用数据分析和自然语言响应来对非结构化的提示或查询提供逻辑响应。 LLM可能会回答一个问题：“历史上四个最伟大的放克乐队是什么？”列出了四个这样的频段，并且对为什么它们是最好的列表，但是标准的计算机程序将无法识别此提示。
但是，LLMS提供的信息的准确性仅与它们消耗的数据一样好。如果给出错误的信息，他们将以误导性信息响应用户查询。 LLMS也会偶尔“幻觉”，在无法提供精确响应时制作事实。例如，2022年新闻媒体快速公司向Chatgpt询问了特斯拉最近的财务季度。尽管Chatgpt以可理解的新闻作品做出了回应，但其中很大一部分信息得到了弥补。
答：由于其并行性和容量，变压器体系结构被广泛用于LLMS，从而使语言模型可以比较数十亿甚至数万亿个参数。
现有的LLM可以广泛分为三种类型：编码器，因果解码器和前缀解码器。
基于Vanilla Transformer模型，编码器构建结构由两个变压器块的堆栈组成 - 一个编码器和解码器。
编码器利用堆叠的多头自发层来编码输入序列并生成潜在表示。解码器对这些表示形式进行了交叉注意，并生成目标序列。
T5和BART等编码器plm在各种NLP任务中都表现出有效性。但是，使用此体系结构构建了只有几个LLM，例如Flan-T5。
因果解码器体系结构包含了单向关注面膜，使每个输入令牌只能参加过去的令牌和本身。解码器以相同的方式处理输入和输出令牌。
包括GPT-1，GPT-2和GPT-3在内的GPT系列模型是建立在该体系结构上的代表性语言模型。 GPT-3显示了出色的内在学习能力。
包括OPT，BLOOM和GOPHER在内的各种LLM都有广泛采用的因果解码器。
前缀解码器结构（也称为非毒物解码器）修改了因果解码器的掩盖机制，以使前缀令牌的双向关注和对产生的代币的单向关注。
像编码器架构一样，前缀解码器可以双向编码前缀序列，并使用共享参数预测自动加压的输出令牌。
一种实用的方法不是从头开始训练，而是训练因果解码器，并将其转换为前缀解码器，以更快地收敛。基于前缀解码器的LLM包括GLM130B和U-PALM。
所有三种体系结构类型都可以使用Experts（MOE）缩放技术进行扩展，该技术稀少地激活了每个输入的神经网络权重的子集。
这种方法已用于Switch Transformer和Glam之类的模型中，增加专家数量或总参数大小已显示出显着的性能改善。
仅编码器架构仅使用变压器块的编码器堆栈，重点是通过自我注意的机制理解和表示输入数据。该体系结构是需要分析和解释文本而不是生成文本的任务的理想选择。
关键特征：
仅编码模型的示例：
答案：已知大型语言模型（LLM）具有“幻觉”。这是一种行为，因为模型说虚假的知识好像是准确的。大型语言模型是训练有素的机器学习模型，该模型根据您的提示生成文本。该模型的培训提供了我们提供的培训数据得出的一些知识。很难说出模型记忆的知识或没有什么知识。当模型生成文本时，它无法确定生成是否准确。
在LLMS的背景下，“幻觉”是指该模型产生错误，荒谬或虚幻的文本的现象。由于LLMS不是数据库或搜索引擎，因此他们不会引用其响应所在的位置。这些模型从您提供的提示中产生文本作为推断。外推的结果不一定得到任何培训数据的支持，而是提示与提示最相关的结果。
LLMS中的幻觉并不比这更复杂，即使模型更复杂。从高水平来看，幻觉是由有限的上下文理解引起的，因为该模型必须将提示和训练数据转换为抽象，其中可能会丢失一些信息。此外，训练数据中的噪声还可能提供偏斜的统计模式，该模式导致模型以您预期的方式做出响应。
答：幻觉可以看作是巨大语言模型的特征。如果您希望这些模型具有创造力，则希望看到它们具有幻觉。例如，如果您要求Chatgpt或其他大型语言模型为您提供幻想故事情节，则希望它创建一个新鲜的角色，场景和故事情节，而不是复制已经存在的故事。只有在模型未搜索培训数据时，这是可行的。
在寻求多样性时，例如在征求思想时，您也可能需要幻觉。这类似于要求模型为您提出想法。尽管不完全相同，但您想就培训集中发现的当前概念提供变体。幻觉使您可以考虑替代选择。
许多语言模型具有“温度”参数。您可以使用API而不是Web界面来控制CHATGPT中的温度。这是一个随机参数。更高的温度会引入更多的幻觉。
答：语言模型不是数据库或搜索引擎。幻想是不可避免的。激怒我的是，这些模型会在文本中产生难以找到的错误。
如果通过污染的培训数据提出了妄想，则可以清理数据并重新训练模型。然而，大多数模型太大而无法独立训练。使用商品硬件可以使甚至不可能微调建立的模型。如果出现了可怕的错误，要求模型再生并在结果中包括人类将是最好的减轻措施。
受控创造是防止幻觉的另一种方法。它需要在提示中提供足够的信息和限制。因此，模型的幻觉能力受到限制。及时工程用于定义模型的角色和上下文，指导一代并防止无限的幻觉。
另请阅读：减轻LLMS幻觉的前7个策略
答案：及时工程是人工智能自然语言处理领域的一种实践，文本描述了AI需要做什么。在此输入的指导下，AI生成输出。该输出可能采取不同的形式，目的是在对话上使用人为理解的文本与模型进行通信。由于任务描述嵌入了输入中，因此模型具有更灵活的可能性。
答：提示是对模型预期的所需输出的详细描述。它们是用户和AI模型之间的交互。这应该使我们更好地了解什么工程。
答：提示的质量至关重要。有一些方法可以改善它们并使您的模型改善产出。让我们看看下面的一些提示：
另请阅读：17个提示提示您的LLM的技术
答：以书面提示使用不同的技术。他们是骨干。
零射击提供了一个提示，该提示不属于培训的一部分，但仍可以根据需要进行。简而言之，LLM可以概括。
例如：如果提示为：将文本分类为中性，负面或阳性。文字是：我认为演讲很棒。
情绪：
输出：积极
“情感”含义的知识使模型零射门如何分类该问题，即使没有给出一堆文本分类。由于文本中未提供描述性数据，因此可能存在陷阱。然后，我们可以使用很少的弹药提示。
在基本的理解中，少数拍摄了必须做的几个示例（镜头）。这可以从演示中进行一些见解。它不仅依靠它的训练，而是建立在可用的镜头上。
COT允许模型通过中间推理步骤实现复杂的推理。它涉及创建和改进称为“推理链”的中间步骤，以促进更好的语言理解和输出。它可能就像是混合动力，将更复杂的任务结合在一起。
答案：检索功能生成（RAG）是优化大型语言模型的输出的过程，因此在产生响应之前，它在其培训数据源之外引用了权威知识库。大型语言模型（LLMS）经过大量数据的培训，并使用数十亿个参数来为诸如回答问题，翻译语言和完成句子的任务生成原始输出。 RAG将LLM的功能扩展到特定领域或组织的内部知识库，而无需重新训练模型。这是提高LLM输出的一种经济高效的方法，因此在各种情况下仍然相关，准确且有用。
答：智能聊天机器人和其他涉及自然语言处理的应用程序（NLP）依靠LLM作为基本人工智能（AI）技术。目的是开发机器人，通过交叉引用可靠的知识源可以在各种情况下响应用户查询。遗憾的是，由于LLM技术的性质，LLM答复变得不可预测。 LLM培训数据还引入了其所拥有的信息且停滞不前的截止日期。
LLM的已知挑战包括：
可以将大型语言模型与过度热心的新员工进行比较，后者拒绝跟上时事，但总是会完全保证对询问做出回应。不幸的是，您不希望您的聊天机器人采用这种心态，因为它可能会损害消费者的信任！
解决其中一些问题的一种方法是抹布。它重新安排LLM以从可靠的，预先选择的知识源中获取相关数据。用户了解LLM如何创建响应，组织对所得文本输出有更多控制权。
答案：生成AI实施中的抹布技术
答：一个名为Langchain的开源框架基于大语言模型（LLMS）创建应用程序。大量的LLM的大型深度学习模型已在大量数据上进行了预培训，可以为用户请求提供答案，例如从基于文本的提示中生成图像或提供查询答案。为了增加模型生成的数据的相关性，准确性和程度，Langchain提供了抽象和工具。例如，开发人员可以使用Langchain组件创建新的提示链或更改预先存在的模板。此外，Langchain的零件使LLM使用新鲜的数据集而无需重新训练。
答案：Langchain：增强机器学习应用程序
答：基于大语言模型（LLM）的应用程序的数据框架称为LlamainDex。大规模的公共数据集用于预先培训LLM，例如GPT-4，这使他们可以开箱即用。然而，在没有您的个人信息的情况下，它们的有用性受到限制。
LlamainDex使用适应性数据连接器，使您可以从数据库，PDF，API等导入数据。该数据的索引导致llm优化的中间表示。之后，LlamainDex通过聊天界面，查询引擎和具有LLM功能的数据代理启用自然语言查询和与您的数据进行通信。您的LLM可以与其大规模访问和分析机密数据，而无需使用更新的数据重新训练模型。
答案：LlamainDex使用检索功能增强的一代（RAG）技术。它将私人知识基础与大型语言模型相结合。索引和查询阶段通常是其两个阶段。
在索引阶段，LlamainDex将有效地将私人数据索引到矢量指数。该阶段有助于建立特定于领域的可搜索知识库。可以输入文本文档，数据库条目，知识图和其他类型的数据。
本质上，索引将数据转换为代表其语义内容的数值嵌入或向量。它允许在整个内容中快速搜索相似之处。
根据用户的问题，RAG管道在查询过程中寻找最相关的数据。然后向LLM提供此数据和查询以生成正确的结果。
通过此过程，LLM可以在第一次培训中获得最新的相关材料。在这一点上，主要问题是在潜在的许多信息源中检索，组织和推理。
答：虽然预先训练的语言模型令人震惊，但它们并不是任何特定任务中的天生专家。 They may have an incredible grasp of language. Still, they need some LLMs fine-tuning, a process where developers enhance their performance in tasks like sentiment analysis, language translation, or answering questions about specific domains. Fine-tuning large language models is the key to unlocking their full potential and tailoring their capabilities to specific applications
Fine-tuning is like providing a finishing touch to these versatile models. Imagine having a multi-talented friend who excels in various areas, but you need them to master one particular skill for a special occasion. You would give them some specific training in that area, right? That's precisely what we do with pre-trained language models during fine-tuning.
Also Read: Fine-Tuning Large Language Models
Answer: While pre-trained language models are remarkable, they are not task-specific by default. Fine-tuning large language models is adapting these general-purpose models to perform specialized tasks more accurately and efficiently. When we encounter a specific NLP task like sentiment analysis for customer reviews or question-answering for a particular domain, we need to fine-tune the pre-trained model to understand the nuances of that specific task and domain.
The benefits of fine-tuning are manifold. Firstly, it leverages the knowledge learned during pre-training, saving substantial time and computational resources that would otherwise be required to train a model from scratch. Secondly, fine-tuning allows us to perform better on specific tasks, as the model is now attuned to the intricacies and nuances of the domain it was fine-tuned for.
Answer: Fine-tuning is a technique used in model training, distinct from pre-training, which is the initializing model parameters. Pre-training begins with random initialization of model parameters and occurs iteratively in two phases: forward pass and backpropagation. Conventional supervised learning (SSL) is used for pre-training models for computer vision tasks, such as image classification, object detection, or image segmentation.
LLMs are typically pre-trained through self-supervised learning (SSL), which uses pretext tasks to derive ground truth from unlabeled data. This allows for the use of massively large datasets without the burden of annotating millions or billions of data points, saving labor but requiring large computational resources. Fine-tuning entails techniques to further train a model whose weights have been updated through prior training, tailoring it on a smaller, task-specific dataset. This approach provides the best of both worlds, leveraging the broad knowledge and stability gained from pre-training on a massive set of data and honing the model's understanding of more detailed concepts.
Answer: Fine-tuning Approaches in Generative AI
Parameter-Efficient Fine-Tuning (PEFT) is a method designed to optimize the fine-tuning process of large-scale pre-trained language models by updating only a small subset of parameters. Traditional fine-tuning requires adjusting millions or even billions of parameters, which is computationally expensive and resource-intensive. PEFT techniques, such as low-rank adaptation (LoRA), adapter modules, or prompt tuning, allow for significant reductions in the number of trainable parameters. These methods introduce additional layers or modify specific parts of the model, enabling fine-tuning with much lower computational costs while still achieving high performance on targeted tasks. This makes fine-tuning more accessible and efficient, particularly for researchers and practitioners with limited computational resources.
Supervised Fine-Tuning (SFT) is a critical process in refining pre-trained language models to perform specific tasks using labelled datasets. Unlike unsupervised learning, which relies on large amounts of unlabelled data, SFT uses datasets where the correct outputs are known, allowing the model to learn the precise mappings from inputs to outputs. This process involves starting with a pre-trained model, which has learned general language features from a vast corpus of text, and then fine-tuning it with task-specific labelled data. This approach leverages the broad knowledge of the pre-trained model while adapting it to excel at particular tasks, such as sentiment analysis, question answering, or named entity recognition. SFT enhances the model's performance by providing explicit examples of correct outputs, thereby reducing errors and improving accuracy and robustness.
Reinforcement Learning from Human Feedback (RLHF) is an advanced machine learning technique that incorporates human judgment into the training process of reinforcement learning models. Unlike traditional reinforcement learning, which relies on predefined reward signals, RLHF leverages feedback from human evaluators to guide the model's behavior. This approach is especially useful for complex or subjective tasks where it is challenging to define a reward function programmatically. Human feedback is collected, often by having humans evaluate the model's outputs and provide scores or preferences. This feedback is then used to update the model's reward function, aligning it more closely with human values and expectations. The model is fine-tuned based on this updated reward function, iteratively improving its performance according to human-provided criteria. RLHF helps produce models that are technically proficient and aligned with human values and ethical considerations, making them more reliable and trustworthy in real-world applications.
Answer: Parameter efficient fine-tuning (PEFT) is a method that reduces the number of trainable parameters needed to adapt a large pre-trained model to specific downstream applications. PEFT significantly decreases computational resources and memory storage needed to yield an effectively fine-tuned model, making it more stable than full fine-tuning methods, particularly for Natural Language Processing (NLP) use cases.
Partial fine-tuning, also known as selective fine-tuning, aims to reduce computational demands by updating only the select subset of pre-trained parameters most critical to model performance on relevant downstream tasks. The remaining parameters are “frozen,” ensuring they will not be changed. Some partial fine-tuning methods include updating only the layer-wide bias terms of the model and sparse fine-tuning methods that update only a select subset of overall weights throughout the model.
Additive fine-tuning adds extra parameters or layers to the model, freezes the existing pre-trained weights, and trains only those new components. This approach helps retain stability of the model by ensuring that the original pre-trained weights remain unchanged. While this can increase training time, it significantly reduces memory requirements because there are far fewer gradients and optimization states to store. Further memory savings can be achieved through quantization of the frozen model weights.
Adapters inject new, task-specific layers added to the neural network and train these adapter modules in lieu of fine-tuning any of the pre-trained model weights. Reparameterization-based methods like Low Rank Adaptation (LoRA) leverage low-rank transformation of high-dimensional matrices to capture the underlying low-dimensional structure of model weights, greatly reducing the number of trainable parameters. LoRA eschews direct optimization of the matrix of model weights and instead optimizes a matrix of updates to model weights (or delta weights), which is inserted into the model.
Answer: Prompt Engineering: Used when you have a small amount of static data and need quick, straightforward integration without modifying the model. It is suitable for tasks with fixed information and when context windows are sufficient.
Retrieval Augmented Generation (RAG): Ideal when you need the model to generate responses based on dynamic or frequently updated data. Use RAG if the model must provide grounded, citation-based outputs.
Fine-Tuning: Choose this when specific, well-defined tasks require the model to learn from input-output pairs or human feedback. Fine-tuning is beneficial for personalized tasks, classification, or when the model's behavior needs significant customization.
Answer: SLMs are essentially smaller versions of their LLM counterparts. They have significantly fewer parameters, typically ranging from a few million to a few billion, compared to LLMs with hundreds of billions or even trillions. This differ
Answer: Like LLMs, SLMs are trained on massive datasets of text and code. However, several techniques are employed to achieve their smaller size and efficiency:
Answer: Here are some examples of SLMs:
While SLMs typically have a few hundred million parameters, some larger models with 1-3 billion parameters can also be classified as SLMs because they can still be run on standard GPU hardware. Here are some of the examples of such models:
Answer: One benefit of Small Language Models (SLMs) is that they may be trained on relatively small datasets. Their low size makes deployment on mobile devices easier, and their streamlined structures improve interpretability.
The capacity of SLMs to process data locally is a noteworthy advantage, which makes them especially useful for Internet of Things (IoT) edge devices and businesses subject to strict privacy and security requirements.
However, there is a trade-off when using small language models. SLMs have more limited knowledge bases than their Large Language Model (LLM) counterparts because they were trained on smaller datasets. Furthermore, compared to larger models, their comprehension of language and context is typically more restricted, which could lead to less precise and nuanced responses.
Answer: The idea of the diffusion model is not that old. In the 2015 paper called “Deep Unsupervised Learning using Nonequilibrium Thermodynamics”, the Authors described it like this:
The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data.
The diffusion process is split into forward and reverse diffusion processes. The forward diffusion process turns an image into noise, and the reverse diffusion process is supposed to turn that noise into the image again.
Answer: The forward diffusion process is a Markov chain that starts from the original data x and ends at a noise sample ε. At each step t, the data is corrupted by adding Gaussian noise to it. The noise level increases as t increases until it reaches 1 at the final step T.
Answer: The reverse diffusion process aims to convert pure noise into a clean image by iteratively removing noise. Training a diffusion model is to learn the reverse diffusion process to reconstruct an image from pure noise. If you guys are familiar with GANs, we're trying to train our generator network, but the only difference is that the diffusion network does an easier job because it doesn't have to do all the work in one step. Instead, it uses multiple steps to remove noise at a time, which is more efficient and easy to train, as figured out by the authors of this paper.
Answer: The noise schedule is a critical component in diffusion models, determining how noise is added during the forward process and removed during the reverse process. It defines the rate at which information is destroyed and reconstructed, significantly impacting the model's performance and the quality of generated samples.
A well-designed noise schedule balances the trade-off between generation quality and computational efficiency. Too rapid noise addition can lead to information loss and poor reconstruction, while too slow a schedule can result in unnecessarily long computation times. Advanced techniques like cosine schedules can optimize this process, allowing for faster sampling without sacrificing output quality. The noise schedule also influences the model's ability to capture different levels of detail, from coarse structures to fine textures, making it a key factor in achieving high-fidelity generations.
Answer: Advanced artificial intelligence (AI) systems known as multimodal large language models (LLMs) can interpret and produce various data types, including text, images, and even audio. These sophisticated models combine natural language processing with computer vision and occasionally audio processing capabilities, unlike standard LLMs that only concentrate on text. Their adaptability enables them to carry out various tasks, including text-to-image generation, cross-modal retrieval, visual question answering, and image captioning.
The primary benefit of multimodal LLMs is their capacity to comprehend and integrate data from diverse sources, offering more context and more thorough findings. The potential of these systems is demonstrated by examples such as DALL-E and GPT-4 (which can process images). Multimodal LLMs do, however, have certain drawbacks, such as the demand for more complicated training data, higher processing costs, and possible ethical issues with synthesizing or modifying multimedia content. Notwithstanding these difficulties, multimodal LLMs mark a substantial advancement in AI's capacity to engage with and comprehend the universe in methods that more nearly resemble human perception and thought processes.
A. Better handling of long-range dependencies
B. Lower computational cost
C. Smaller model size
D. Easier to interpret
Answer: A. Better handling of long-range dependencies
A. Convolution
B. Recurrence
C. Attention
D. Pooling
Answer: C. Attention
A. To normalize the inputs
B. To provide information about the position of words
C. To reduce overfitting
D. To increase model complexity
Answer: B. To provide information about the position of words
A. They have a fixed vocabulary
B. They are trained on a small amount of data
C. They require significant computational resources
D. They are only suitable for translation tasks
Answer: C. They require significant computational resources
A. VGG16
B. GPT-4
C. ResNet
D. YOLO
Answer: B. GPT-4
A. To reduce their size
B. To adapt them to specific tasks
C. To speed up their training
D. To increase their vocabulary
Answer: B. To adapt them to specific tasks
A. To control the randomness of the model's output
B. To set the model's learning rate
C. To initialize the model's parameters
D. To adjust the model's input length
Answer: A. To control the randomness of the model's output
A. Zero-shot prompting
B. Few-shot prompting
C. A和B
D.以上都没有
Answer: C. Both A and B
A. More deterministic output
B. More creative and diverse output
C. Lower computational cost
D. Reduced model accuracy
Answer: B. More creative and diverse output
A. Faster training times
B. Lower memory usage
C. Improved generation quality by leveraging external information
D. Simpler model architecture
Answer: C. Improved generation quality by leveraging external information
A. To generate the final output
B. To retrieve relevant documents or passages from a database
C. To preprocess the input data
D. To train the language model
Answer: B. To retrieve relevant documents or passages from a database
A. Image classification
B. Text summarization
C. Question answering
D. Speech recognition
Answer: C. Question answering
A. Training from scratch on a new dataset
B. Adjusting the model's architecture
C. Continuing training on a specific task or dataset
D. Reducing the model's size
Answer: C. Continuing training on a specific task or dataset
A. It requires less data
B. It requires fewer computational resources
C. It leverages previously learned features
D.以上所有
Answer: D. All of the above
A. Overfitting
B. Underfitting
C. Lack of computational power
D. Limited model size
Answer: A. Overfitting
A. To enhance the stability of training deep neural networks
B. To generate high-quality images from text descriptions
C. To compress large models
D. To improve the speed of natural language processing
Answer: B. To generate high-quality images from text descriptions
A. Reducing the noise in input data
B. Iteratively refining the generated image to remove noise
C. Simplifying the model architecture
D. Increasing the noise to improve generalization
Answer: B. Iteratively refining the generated image to remove noise
A. Image classification
B. Text generation
C. Image generation
D. Speech recognition
Answer: C. Image generation
In this article, we have seen different interview questions on generative AI that can be asked in an interview. Generative AI now spans a lot of industries, from healthcare to entertainment to personal recommendations. With a good understanding of the fundamentals and a strong portfolio, you can extract the full potential of generative AI models. Although the latter comes from practice, I'm sure prepping with these questions will make you thorough for your interview. So, all the very best to you for your upcoming GenAI interview!
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