Generative AI Flashcards
In the context of LLM prompting explain what chain of thought tree of though and graph of thought are.
In the context of prompting Large Language Models (LLMs), Chain of Thought (CoT), Tree of Thought (ToT), and Graph of Thought (GoT) are techniques used to guide the model’s reasoning process and improve the quality and coherence of the generated responses.
**1. Chain of Thought (CoT):
** - CoT prompting involves providing a series of intermediate reasoning steps or a sequence of thoughts as part of the prompt.
- The model is encouraged to generate a step-by-step explanation or thought process before arriving at the final answer.
- Example prompt: “Question: What is the capital of France? Let’s think step by step:
Step 1: France is a country in Europe.
Step 2: The capital of a country is usually its largest and most important city.
Step 3: Paris is the largest and most well-known city in France.
Therefore, the capital of France is Paris.”
- CoT prompting has been shown to improve the model’s ability to provide more accurate and reasoned responses, especially for tasks that require multiple reasoning steps.
2. Tree of Thought (ToT):
- ToT prompting extends the idea of CoT by organizing the reasoning steps into a tree-like structure.
- Each node in the tree represents a thought or a reasoning step, and the edges represent the dependencies or relationships between the thoughts.
- The model is prompted to generate the tree of thoughts, starting from the root node and expanding to the leaf nodes.
- Example prompt: “Question: What is the capital of France? Let’s build a tree of thoughts:
Root: France
- Node 1: France is a country in Europe.
- Node 2: Capitals
- Node 2.1: The capital of a country is usually its largest and most important city.
- Node 2.2: Paris is the largest and most well-known city in France.
Leaf: Therefore, the capital of France is Paris.”
- ToT prompting allows for more structured and hierarchical reasoning, enabling the model to break down complex problems into smaller subproblems.
3. Graph of Thought (GoT):
- GoT prompting represents the reasoning process as a graph, where nodes represent concepts or entities, and edges represent the relationships or connections between them.
- The model is prompted to generate a graph of thoughts, capturing the relevant concepts and their relationships.
- Example prompt: “Question: What is the capital of France? Let’s create a graph of thoughts:
Nodes: France, Europe, Capital, Paris
Edges:
- France is a country in Europe.
- Capital is the largest and most important city of a country.
- Paris is the largest and most well-known city in France.
- Paris is the capital of France.”
- GoT prompting allows for more flexible and expressive reasoning, enabling the model to capture complex relationships and dependencies between concepts.
These prompting techniques aim to provide more structure and guidance to the model’s reasoning process, encouraging it to generate more coherent, logical, and interpretable responses. By explicitly modeling the intermediate steps or relationships, CoT, ToT, and GoT prompting can improve the model’s performance on tasks that require complex reasoning, such as multi-step question answering, logical inference, and decision making.
In the context of LLM prompting exaplin what we mean by in context learning
In-context learning is a method of prompt engineering that allows language models to learn tasks from a few examples.
What is RAG?
RAG stands for Retrieval-Augmented Generation. It is a model architecture that combines the benefits of retriever models and generator models for tasks like question answering or dialogue.
In a RAG model, when a query (like a question) is input, a retriever component first identifies relevant context documents from a large corpus of knowledge. These documents are then provided as input to a generator model which produces a response.
The key idea behind RAG is to allow the model to dynamically select relevant information at inference time. This is in contrast to a more traditional approach where a model is trained on a fixed set of documents and cannot incorporate new information after training.
RAG models can be implemented in various ways depending on the specifics of the retriever and generator components. For example, the retriever could be a simple nearest-neighbor model or a more complex transformer-based model, and the generator could be a standard seq2seq model or a large language model.
In LLMs what are adaptors?
In the context of Large Language Models (LLMs), “adaptors” refer to lightweight, trainable modules that are added to a pre-trained model to adapt it to specific tasks without modifying the original model parameters. Adaptors allow for task-specific fine-tuning while preserving the knowledge encoded in the pre-trained model.
An adaptor is usually a small neural network that is inserted between the layers of the pre-trained model. For example, in a transformer-based LLM, an adaptor can be added after the self-attention and feed-forward layers within each transformer block.
The primary advantage of using adaptors is that they require fewer parameters to be updated during fine-tuning, which reduces the risk of overfitting, especially when the amount of task-specific data is small. It also makes it possible to use a single pre-trained model for multiple tasks simultaneously by using different adaptors for each task, which can save computational resources.
However, adaptors may not always provide the same level of performance as full fine-tuning, particularly for tasks that are very different from the original pre-training task. The choice between using adaptors and full fine-tuning depends on factors such as the amount of task-specific data, computational resources, and the similarity between the pre-training and fine-tuning tasks.
In the LLMS context what is LORA?
Low-Rank Adaptation of Large Language Models
LORA differs from adapters in its approach to modifying the model. Instead of adding new trainable parameters in the form of small neural networks (as adapters do), LORA adds low-rank matrices to the pre-existing weight matrices in the transformer layers of the model. These low-rank matrices are the only parameters that are fine-tuned during adaptation.
The key idea behind LORA is to limit the fine-tuning to a low-dimensional subspace of the parameter space, thereby making the adaptation more parameter-efficient and reducing the risk of overfitting when task-specific data is limited.
In the original LORA paper, it is shown that LORA can achieve comparable or even superior performance to full model fine-tuning on a variety of NLP tasks, while only fine-tuning a small fraction of the parameters. This makes LORA particularly useful for adapting large models where full fine-tuning is computationally prohibitive.
More specifically, in the context of a Transformer model, each layer’s original weight matrix W in the feed-forward network is augmented with a low-rank matrix UV^T, where U and V are the parameters to be fine-tuned, and their dimensions are determined by the rank of the low-rank matrix.
During fine-tuning, only the parameters of the low-rank matrices (U and V) are updated, while the original pre-trained parameters are kept frozen. This effectively constrains the fine-tuning to a low-dimensional subspace of the parameter space, reducing the risk of overfitting when task-specific data is limited.
Benefits of LORA:
Parameter Efficiency: LORA only fine-tunes a small fraction of the parameters, making it more parameter-efficient than full fine-tuning.
Reduced Overfitting: By constraining the fine-tuning to a low-dimensional subspace, LORA reduces the risk of overfitting, especially when the amount of task-specific data is small.
Performance: Despite its efficiency, LORA has been shown to achieve comparable or even superior performance to full fine-tuning on a variety of tasks.
The purpose of LORA is similar to that of adapters: both aim to adapt pre-trained models to specific tasks in a parameter-efficient manner. However, the methods they use to achieve this are different. While adapters add new trainable parameters in the form of small neural networks, LORA adds low-rank matrices to the existing weight matrices. The choice between LORA and adapters would depend on the specific requirements of the task and the resources available.
What is the QLORA approach used in LLM?
QLORA stands for Quantized Layer-wise Optimal Relevance Approximation. It is a variant of LORA designed to further reduce the memory footprint and computational requirements of fine-tuning large language models.
In QLORA, the low-rank matrices added to the weight matrices in each transformer layer of a pre-trained language model are quantized. Quantization is a process that reduces the number of bits that represent a number. In the context of neural networks, quantization can significantly reduce the memory and computational requirements, at the cost of a slight decrease in model accuracy.
QLORA applies quantization to the low-rank matrices U and V in LORA. By doing so, it further reduces the number of parameters that need to be stored and fine-tuned, leading to even more efficient adaptation of pre-trained models.
However, it’s important to note that the quantization process can introduce some level of approximation error. Therefore, there is a trade-off between the efficiency gains from quantization and the potential decrease in model performance. In practice, this trade-off needs to be carefully managed to ensure that the benefits of quantization outweigh the potential downsides.
What is RLHF reinforcment learning from human feedback?
Reinforcement Learning from Human Feedback (RLHF) is a technique to train AI models where traditional reinforcement learning is combined with valuable feedback from human evaluators.
To break down the process technically:
Initial Policy Training: An initial policy is trained using supervised learning, where human experts demonstrate correct behavior in the task environment. This is called the “expert policy”.
Data Collection: The model interacts with the environment (or a simulation of it) based on the current policy and collects trajectories of states, actions, and rewards.
Reward Modeling: Human evaluators compare different actions taken by the model in various states and rank them according to their preferences. These comparisons are used to create a reward model. It’s important to note that the humans are not rating the absolute value of an action, but rather making relative comparisons between different actions.
Policy Optimization: The model’s policy is then updated to maximize the expected cumulative reward as per the reward model. This is typically done using Proximal Policy Optimization (PPO) or similar algorithms.
Iteration: Steps 2 through 4 are repeated iteratively, refining the model’s behavior over time with continuous feedback from humans.
The RLHF process can be computationally intensive and time-consuming due to the need for continuous human involvement in reward modeling. However, it’s an effective way to train models in complex environments where it’s hard to define a suitable reward function or in scenarios where exploration can have high costs.
what other techniques are there apart from RLHF?
What is the difference between Factual Grounding vs RAG?
Factual Grounding and Retrieval-Augmented Generation (RAG) are both approaches to enhance language models with external knowledge.
RAG, which stands for Retrieval-Augmented Generation,** is a specific approach to enhancing factual grounding in generative models. **It is a framework that combines a pre-trained language model with a retrieval system.
Factual grounding can be achieved with various mechanisms, including retrieval systems, knowledge bases, or even fine-tuning on factually verified datasets it can even use real time queries at inference time.
What is Flare Forward-looking Active Retrieval Augmented Generation and how does it differ from RAG?
Flare (Forward-looking Active Retrieval) is an extension of the Retrieval-Augmented Generation (RAG) approach that aims to improve the efficiency and effectiveness of the retrieval process. While RAG focuses on retrieving relevant documents based on the current input query, Flare introduces a forward-looking mechanism to actively retrieve documents that are likely to be relevant for future generation steps.
Key differences between Flare and RAG:
Forward-looking Retrieval:
RAG: Retrieves documents based on the current input query only.
Flare: Introduces a forward-looking retrieval mechanism that considers the potential relevance of documents for future generation steps.
Active Retrieval:
RAG: Performs retrieval once based on the input query.
Flare: Actively retrieves documents at each generation step, taking into account the previously generated tokens and the current context.
Retrieval Strategy:
RAG: Uses a fixed retrieval strategy based on similarity scores between the query and document embeddings.
Flare: Employs a learned retrieval strategy that adapts based on the current generation context and the previously retrieved documents.
Efficiency:
RAG: Requires retrieving relevant documents for each input query, which can be computationally expensive.
Flare: Optimizes the retrieval process by actively selecting documents that are most likely to be relevant for the current and future generation steps, reducing the number of retrieval operations.
Implementation details of Flare:
Retrieval Model: Flare uses a learned retrieval model that takes the current generation context and previously retrieved documents as input and outputs a probability distribution over the documents in the corpus.
Document Encoding: The documents in the corpus are encoded into dense vector representations using an encoder (e.g., BERT) offline, similar to RAG.
Query Encoding: At each generation step, the current input query and the previously generated tokens are encoded using the same encoder to obtain a query vector.
Retrieval Probabilities: The retrieval model computes the probability of each document being relevant based on the query vector and the document vectors.
Active Retrieval: The top-k documents with the highest retrieval probabilities are actively retrieved at each generation step.
Generation: The retrieved documents and the current generation context are fed into the generative language model (e.g., GPT) to generate the next token.
Retrieval Loss: Flare introduces a retrieval loss that encourages the retrieval model to select documents that are likely to be relevant for future generation steps. This loss is typically based on the likelihood of the retrieved documents given the future generated tokens.
Flare has shown improved performance and efficiency compared to RAG on various language generation tasks, such as open-domain question answering and dialogue systems. By actively retrieving documents that are likely to be relevant for future generation steps, Flare reduces the number of retrieval operations and enhances the quality of the generated responses.
What are sparse mixture of experts models (SMoE)
**Sparse Mixture of Experts (SMoE) **models are a type of neural network architecture that combines the concept of mixture of experts with sparsely activated subnetworks. The goal is to improve efficiency and scalability by selectively activating a subset of experts for each input.
Architecture:
SMoE consists of a set of expert networks and a gating network.
Each expert network is a specialized subnetwork capable of handling a specific subset of the input space.
The gating network is responsible for assigning input examples to the appropriate experts based on their characteristics.
The experts are sparsely activated, meaning that for each input, only a small subset of experts (e.g., top-k) are selected and computed.
Implementation:
- Expert Networks: Design a set of expert networks, each being a neural network (e.g., MLP or transformer) with its own parameters.
- Gating Network: Implement a gating network that takes the input and outputs a probability distribution over the experts. This can be achieved using a softmax layer.
- Sparse Activation: Select the top-k experts with the highest probabilities from the gating network for each input. Only compute the forward pass for these selected experts.
- Combination: Combine the outputs of the selected experts using a weighted sum, where the weights are determined by the gating network probabilities.
- Training: Train the SMoE model using standard optimization techniques, such as stochastic gradient descent, to minimize the loss function.
* Pros:
Improved efficiency by selectively activating a subset of experts for each input, reducing computational cost.
Increased model capacity and expressiveness by allowing different experts to specialize in different parts of the input space.
Scalability to large-scale datasets and complex tasks by distributing the workload among multiple experts.
Cons:
* Routing Ambiguity: The gating network may have difficulty deciding which experts to activate for certain inputs, which can lead to suboptimal performance if not properly managed.
* Increased model complexity due to the presence of multiple expert networks and the gating mechanism.
* Potential challenges in training, such as ensuring balanced expert utilization and preventing individual experts from dominating.
* Overhead in terms of memory and communication costs due to the need to store and coordinate multiple expert networks.
* Mathematical Details:
Gating Network: The gating network outputs a probability distribution over the experts using a softmax function: p_i = exp(g_i) / sum_j exp(g_j), where g_i is the logit for expert i.
Sparse Activation: Select the top-k experts based on the gating probabilities. The output of the SMoE is computed as: y = sum_i p_i * e_i(x), where e_i(x) is the output of expert i for input x.
* Loss Function: The loss function can be a combination of the task-specific loss (e.g., cross-entropy for classification) and additional regularization terms to encourage balanced expert utilization and sparsity.
SMoE models have been successfully applied to various tasks, including language modeling, machine translation, and computer vision, demonstrating improved efficiency and performance compared to traditional dense models.
What are some example of sparse mixture of experts models?
**Mixtral 8x7B
**Gshard: Developed by Google, Gshard is a model that uses a MoE architecture for efficient scaling. It uses a gating mechanism to determine which experts to use for each token in the input.
Switch Transformer: Also developed by Google, the Switch Transformer is another example of a MoE model. It achieves high efficiency and scalability by dynamically routing input tokens to a subset of experts, reducing the computational cost.
Turing-NLG: Microsoft’s Turing-NLG, a 17-billion parameter language model, also uses a MoE architecture to efficiently distribute its parameters.
What are some prons and cons of sparse mixture of experts large models?
Sparse Mixture of Experts (MoE) models, where each input is processed by only a small subset of experts, have several advantages and disadvantages.
Pros:
1. Capacity: MoE models can have a much larger capacity than standard models, as they effectively contain many smaller models (the experts) that can each learn different things. This can lead to better performance on complex tasks.
1. Efficiency: Because each input is processed by only a few experts, MoE models can be more efficient than standard models. This is particularly true when expert parallelism is used, allowing different experts to be processed on different devices simultaneously.
1. Adaptability: MoE models can potentially adapt better to different types of input data, as different experts can specialize in different types of data or tasks.
Cons:
* Complexity: MoE models are more complex than standard models, both in terms of their architecture and their training process. This can make them harder to implement and debug.
* Overfitting: Because of their larger capacity, MoE models can be more prone to overfitting, especially if the number of experts is large relative to the amount of training data.
* Load Balancing: Ensuring that the computational load is evenly distributed across experts can be challenging, especially when some experts are used more than others. This can lead to inefficient use of computational resources.
* Training Difficulty: Training MoE models can be difficult due to issues such as expert imbalance (where some experts are used much more than others), and the need to train both the experts and the gating network that selects experts. Furthermore, traditional training methods like batch normalization do not work well with MoE models, requiring the development of new methods.
When compared to similar models that do not use Sparse Mixture of Experts (MoE), MoE models have distinct trade-offs in terms of memory usage, speed, latency, and throughput:
* Memory: MoE models can potentially use less memory per input because each input is only processed by a subset of the experts. However, the total memory usage might still be high due to the large number of parameters across all experts.
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* Speed/Latency: The speed or latency of MoE models can be higher than traditional models if the gating network operation and the selection of experts is not optimized. However, with a well-implemented expert parallelism, the speed can be significantly improved as different experts can operate simultaneously on different hardware resources.
* Throughput: In terms of throughput, MoE models can potentially handle larger volumes of data more efficiently, thanks to expert parallelism. Each expert can process different portions of the data simultaneously, leading to higher overall throughput.
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* Computational Cost: While theoretically, MoE models can be more efficient, the computational cost could be higher in practice due to the overhead of managing multiple experts and the gating network. This includes the cost of load balancing and coordinating between different devices in a distributed setup.
In mixture of expert implementeation of LLMs what is expert parallelism?
Expert parallelism in the context of Mixture of Experts (MoE) implementation in Large Language Models (LLMs) refers to the ability to distribute the computation load of individual experts across multiple devices or processors.
In a MoE model, each ‘expert’ is a smaller neural network that specializes in a particular type of data or pattern. Instead of running all computations on a single device, expert parallelism allows each expert to be computed on a different device. This is particularly useful when the number of experts is large, as it can significantly speed up computation.
The key benefit of expert parallelism is the potential for increased model capacity without a linear increase in computational cost. Because the experts can operate independently and simultaneously, larger models can be trained more efficiently.
However, implementing expert parallelism can be challenging due to the need for effective load balancing across devices (since some experts may be used more than others) and for synchronizing the updates from all the devices. Despite these challenges, expert parallelism is a powerful tool for scaling up the training of MoE models in LLMs.
Explain with equation what changes inside a FFN transcformer block with mix of experts routing
In a standard Transformer model, the Feed-Forward Network (FFN) block is a simple two-layer neural network applied independently to each position in the input sequence. It can be represented by the equation:
FFN(x) = max(0, xW1 + b1)W2 + b2
Where W1, W2, b1, and b2 are the weights and biases of the two layers respectively, and x is the input.
When we introduce a mixture of experts (MoE) into the FFN, the setup changes. Instead of having one universal FFN, we now have multiple expert FFNs and a gating network that determines which expert FFN should be used for each input.
The gating network computes a distribution over the experts, based on the input. The output of the MoE is a weighted sum of the expert outputs, with the weights given by the gating network. This can be represented by the following equation:
MoE(x) = Σ (g_i(x) * FFN_i(x))
Here, FFN_i represents the i-th expert FFN, g_i(x) is the gating network’s weight for the i-th expert given input x, and the sum is over all the experts. The gating network is typically a simple feed-forward network with softmax output to ensure the weights sum to one.
The benefit of this setup is that it allows the model to adaptively choose which expert to use for each input, potentially increasing the model’s capacity without a significant increase in computational cost, as only a subset of experts need to be used for each input.
what is SwiGLU Activation Function?
SwiGLU is an intriguing** variant of the Gated Linear Units (GLU)** activation function. Let’s dive into its details:
**What is SwiGLU?
**SwiGLU is a non-linear activation function that builds upon the GLU concept.
It employs the Swish function as its core activation mechanism.
The Swish function is defined as: Swish(x) = x * Sigmoid(x).
SwiGLU combines this Swish activation with a gating mechanism.
SwiGLU Activation Formula:
Given input x, weight matrices W and V, bias terms b and c, and a scaling factor β, SwiGLU is defined as:
SwiGLU(x, W, V, b, c, β) = Swish β (xW + b) ⊗ (xV + c)
Why SwiGLU?
Efficiency: SwiGLU offers computational efficiency while maintaining expressive power.
Non-linearity: It introduces non-linearity, which is crucial for deep neural networks.
Performance: SwiGLU has demonstrated competitive performance in various tasks, especially language modeling.
In the context of transformer architecture what is KV-Cache?
