How to Engage, Instruct, and Dialogue for Maximum Impact

Language models have revolutionised the field of natural language processing, enabling machines to understand and generate human-like text. Among the various types of language models, three distinct categories: Generic (or Raw) Language Models, Instruction-Tuned Language Models, and Dialogue-Tuned Language Models, possesses unique characteristics and applications, making them valuable tools in different contexts. In this article, we delve into these types of LLMs and explore their potential, using Netflix as a practical example to illustrate their capabilities.

Generic Language Models

Generic language models, sometimes referred to as raw language models, are designed to generate text based on patterns and general knowledge learned during training. These models excel at providing information, explanations, and summaries without specific instructions or constraints. In the context of Netflix, a generic language model can offer insights on topics such as the streaming industry, content production, or technology advancements that have shaped the platform's success. These predict the next word (technically token) based on the language in the training data.

• "Explain the concept of content streaming and its impact on the entertainment industry."
• "What are the key factors to consider when developing a successful video-on-demand platform like Netflix?"
• "Discuss the evolution of original content production in the streaming era."

Where do Generic LLM's fall short ?

A generic LLM might generate output that is undesirable to human that is interacting with the model and may cause alignment issues. Such as :-

• Prevent our LLM from being racist.
• Avoid generation of factually incorrect output.

Along with that, they may not be an expert in particular domain to serve as an expert. This is to ensure LLM's output adheres to three basic principals, namely:-

• Quality:?an average of sensibleness (does the model make sense and not contradict earlier dialog?), specificity (is the model’s response specific to the given context?), and interestingness (does the model’s response capture the reader’s attention or arouse curiosity?).
• Safety:?ability to avoid unintended or harmful results that contradict objectives derived from the?Google AI Principles.
• Groundedness:?producing responses that are factually correct and can be associated with authoritative, external sources.

When the fine-tuning approach is applied, we observe that the model achieves significant improvements in quality, safety, and groundedness.

Instruction-tuned Language Model

Instruction-tuned language models are trained with additional supervision or fine-tuning using specific instructions. These models excel at following directions and generating text according to given guidelines. In the case of Netflix, an instruction-tuned language model can provide step-by-step guides, recommendations, or even compose marketing materials for new features or promotions. They can be invaluable in creating user manuals, generating personalised content suggestions, or crafting engaging descriptions for movies and TV shows. They are trained to predict a response to the instructions given in the input.

• "Provide a step-by-step guide on how to sign up for a Netflix account and start streaming."
• "Write a brief overview of Netflix's recommendation algorithm and how it personalises content suggestions for users."
• "Compose a user manual for the Netflix mobile app, highlighting its key features and navigation options."

Dialogue-tuned Language Model

A dialogue-tuned language model is trained to engage in conversational interactions. To prompt this type of model, you can provide a conversation context with alternating user and model responses. It is important to clearly indicate which part of the dialogue is the user input and which part is the model's response. Dialogue-tuned models are a special case of instruction-tuned where requests are typically framed-as-question to chatbot. They are trained to have a dialogue by predicting the next response.

Trained Conversation:

User: "I'm in the mood for a romantic comedy. Any suggestions on Netflix?"

Model: "Absolutely! You might enjoy 'To All the Boys I've Loved Before' or 'Always Be My Maybe.' They are both heartwarming and funny."

Trained Conversation:

User: "Is there a way to download Netflix movies and shows for offline viewing?"

Model: "Yes, you can download content on the Netflix mobile app. Just find the download button on the details page of the movie or TV show you want to watch offline."

The generic, instruction-tuned, and dialogue-tuned language models discussed above each bring unique strengths to the table. As language models continue to advance, their applications will expand, shaping the future of human-machine interaction and transforming industries across the board.

Before we go here's a look at the process to follow while planning a Generative AI project.