Chatbot Architectures Flashcards
What was the primary approach used by early rule-based chatbots?
Early rule-based chatbots relied on predefined scripts to handle interactions.
How do modern chatbots differ from early rule-based ones in handling language?
Modern chatbots leverage machine learning and neural network architectures to understand and generate language.
Which neural network model was initially used in the case study and what was its limitation?
The case study used a Recurrent Neural Network (RNN) which struggled with complex language and long-range dependencies.
What is a Recurrent Neural Network (RNN) and how does it process sequential data?
An RNN is designed for sequential data where each word in a sentence is processed one after another, using a recurrent connection in the hidden layer to retain context from previous time steps.
What key limitation do standard RNNs face when processing long sequences?
Standard RNNs suffer from the vanishing gradient problem, which diminishes gradient signals over long sequences, leading to loss of earlier context.
Why can RNNs be slower to train or infer on very long texts?
Because RNNs process one token at a time sequentially, they cannot fully leverage parallel processing.
What are Long Short-Term Memory (LSTM) networks and how do they improve upon standard RNNs?
LSTMs are a type of RNN that use a complex neuron unit with an internal memory cell and gating mechanisms to retain information over longer sequences, addressing the vanishing gradient problem.
Name the three gates in an LSTM and their primary functions.
The three gates are the input gate (controls new information to add), the forget gate (controls which old information to discard), and the output gate (controls how the cell state affects the current output).
Despite their improvements, what is a drawback of using LSTMs compared to simple RNNs?
LSTMs are more computationally intensive due to their gating calculations and still operate sequentially per time step.
What is the key innovation behind Transformer models in NLP?
Transformers use a self-attention mechanism to process entire sequences in parallel, allowing them to capture long-range relationships more effectively.
How does self-attention in Transformer models benefit language processing?
Self-attention allows the model to weigh the importance of each word relative to all others in the sequence, capturing global context without relying on sequential processing.
What architectural components make up a typical Transformer model?
A typical Transformer consists of an encoder and decoder stack with multiple layers of self-attention heads and feed-forward networks.
Why are Transformers considered more efficient for training on large datasets compared to RNNs/LSTMs?
Transformers can process entire sentences simultaneously in parallel, significantly speeding up computation on modern hardware like GPUs.
What is a notable example of a transformer-based model mentioned in the guide, and what is its significance?
GPT-3 is an example of a transformer-based model known for generating fluent, contextually relevant responses and representing state-of-the-art performance in chatbots.
What are the comparative advantages of Transformers over RNNs?
Transformers process data in parallel, handle long-range dependencies better, train faster on large datasets, and achieve superior performance on language understanding and generation tasks compared to sequential RNNs.
How do LSTMs compare to Transformers in handling long inputs?
LSTMs handle long inputs sequentially with improved memory retention over RNNs, but Transformers capture long-range context more effectively and process inputs faster due to parallelization, albeit at higher computational and data requirements.
What practical trade-off should be considered when upgrading from an RNN to a Transformer-based chatbot architecture?
Upgrading to a Transformer can significantly improve language understanding and generation, but it demands more processing power, requires larger datasets, and needs careful tuning.
