Ask ChatGPT: Seq2Seq vs Causal LLM

I am not sure what to make of this conversation with ChatGPT. Is this what they mean by a "stochastic parrot"? :-)

Choosing LLM Foundation Model

User

I want to create a chatbot using an LLM. Is it better to use a causal LM or a seq2seq LLM as the foundation model?

ChatGPT

Both causal language models (LM) and sequence-to-sequence (seq2seq) language models have their own strengths and applications. The choice between them depends on the specific requirements and goals of your chatbot.

Causal Language Model:

A causal LM, such as the GPT architecture, is designed to predict the next word in a sequence given the previous context. It is suitable for tasks like generating text, completing sentences, or answering questions. Causal LMs excel at generating coherent and contextually relevant responses. If your primary goal is to create a chatbot that generates human-like and contextually appropriate responses, a causal LM can be a good foundation model.

Seq2seq Language Model:

Seq2seq models, on the other hand, are typically used for tasks involving translation, summarization, or generating responses to input sequences. They consist of an encoder network that processes the input sequence and a decoder network that generates the output sequence. Seq2seq models are more focused on capturing the relationship between input and output sequences. If your chatbot's main task involves understanding and generating specific responses based on given input sequences, a seq2seq LM could be a suitable choice.

Considerations for Choosing a Foundation Model:

When deciding between a causal LM and a seq2seq LM as the foundation model for your chatbot, consider the following factors:

1. Use Case: Determine the specific tasks and objectives of your chatbot. If your chatbot needs to generate contextually relevant responses in a conversational manner, a causal LM might be more appropriate. If it requires understanding and generating specific responses based on input sequences, a seq2seq LM could be a better fit.

2. Data Availability: Consider the availability and quality of training data. Causal LMs typically require large amounts of diverse text data for effective training. Seq2seq models might require paired input-output data, which can be challenging to collect or create, depending on the task.

3. Training and Resource Requirements: Seq2seq models often require more resources and computational power for training due to their complex architecture. Causal LMs can be easier to train and fine-tune on large amounts of text data.

4. Customizability: Seq2seq models offer more flexibility in terms of customization. You can design the architecture to suit your specific task and incorporate techniques like attention mechanisms or memory networks. Causal LMs have a fixed architecture but can still be fine-tuned on domain-specific data.

In summary, if your primary goal is to create a conversational chatbot, a causal LM can be a good foundation. If you require more specific input-output mapping, a seq2seq LM might be a better choice. Consider the specific requirements, available data, and desired level of customization when making your decision.

User

Let me elaborate my query a little further. First of all, when I say LLM, I mean transformer based LLM, so things like attention are a given. My chatbot will be task-oriented domain-specific like ordering a pizza. It needs to connect to enterprise back-end systems through APIs to micro-services. Are seq2seq LLMs better suited for multi-turn domain-specific task-oriented dialog compared to causal LLMs. Assume that I have to fine-tune the LLM on domain specific data. Also assume that I will use a smaller version of the large foundation LLM, something that might fit on a small 6GB GPU for fine-tuning and inference.

ChatGPT

Given your specific requirements for a task-oriented, domain-specific chatbot that connects to enterprise back-end systems through APIs, a seq2seq LLM can be a more suitable choice compared to a causal LLM. Seq2seq models have been widely used for tasks like dialogue generation and have shown effectiveness in handling multi-turn conversations.

Here are some reasons why a seq2seq LLM might be better suited for your scenario:

1. Handling Multi-turn Dialog: Seq2seq models, with their encoder-decoder architecture, can effectively capture the context and dependencies between multiple turns in a conversation. They can maintain a dialogue history and generate responses that consider the entire conversation flow. This is particularly useful for a task-oriented chatbot where user requests and system responses need to be processed in a coherent and meaningful manner across multiple turns.

2. Customized Output Generation: Seq2seq models provide more flexibility in generating task-specific outputs. You can design the decoder to incorporate specific constraints, templates, or domain-specific knowledge to generate responses tailored to your task. This allows you to control the output structure and format, making it easier to integrate with enterprise back-end systems or APIs.

3. Fine-tuning on Domain-specific Data: Fine-tuning a seq2seq LLM on domain-specific data is a common approach to improve performance and adapt the model to the target task. By providing your model with relevant domain-specific dialog data, you can enhance its ability to understand and generate responses specific to your task, such as pizza ordering. Fine-tuning helps the model capture domain-specific language patterns and context, leading to more accurate and contextually appropriate responses.

4. Resource Requirements: While seq2seq models, particularly large ones, can be computationally intensive during training, you mentioned that you will be using a smaller version of the large foundation LLM that fits on a 6GB GPU. This implies that you are considering resource constraints. Seq2seq models can be effectively trained and deployed on smaller hardware configurations, making them a feasible option for your scenario.

In summary, for a task-oriented, domain-specific chatbot that connects to enterprise back-end systems through APIs, a seq2seq LLM is likely to be a better fit. It can handle multi-turn dialog, offer flexibility in generating customized responses, and can be fine-tuned on domain-specific data. By using a smaller version of the large foundation LLM, you can ensure it can be trained and deployed within the limitations of a 6GB GPU.