Conversational AI and LLM

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What is Conversational AI?

Conversational AI refers to the use of artificial intelligence technology to enable machines to communicate with people. It understands and interprets what someone says or writes, responding naturally to maintain the conversation. Thanks to recent advancements, machines can now engage in smart and natural conversations with humans.

The Technology Behind Conversational AI

Conversational AI relies on several key components, including speech recognition, intent detection, and generating spoken or written responses. The following elements form the core of the conversational AI technology stack:

Speech-to-Text:

• Converts spoken words into text transcriptions.

Language Processing:

Natural Language Understanding (NLU):

• Helps technology comprehend natural human language, especially in voice interactions where specific keywords may not be used.

Intent:

• Determines the actions triggered by conversational inputs.

Intent Detection:

• Identifies the intent behind an utterance, which is more challenging in voice interactions due to the potential for longer stories.

Value Extraction:

• Extracts relevant information from customer queries and stores it in corresponding ‘slots,’ crucial for handling multiple values in a single speech for natural conversations.

Text-to-Speech (TTS):

• Converts written text into spoken utterances. Off-the-shelf solutions may sound robotic, but using voice actors can make responses sound more natural.

Context and Multi-turn Conversations:

• Bots must maintain context across multiple interactions for natural-feeling conversations, particularly important in voice interactions where chat history isn’t visible.

Dialogue Policy:

• Guides the flow of a conversation, allowing the bot to intelligently navigate interactions. A robust dialogue policy can handle interruptions and enhance the user experience.

What is a Large Language Model (LLM)?

A large language model (LLM) is an AI program capable of recognizing and generating text. Trained on vast datasets, LLMs use machine learning, particularly a type of neural network called a transformer model.

In simple terms, an LLM is a computer program that has been trained on numerous examples to recognize and interpret human language or other complex data. Many LLMs are trained on data from the Internet, involving thousands or millions of gigabytes of text. The quality of these samples impacts the model’s ability to learn natural language, so curated datasets may be used.

LLMs employ deep learning to understand how characters, words, and sentences function together. Deep learning uses probabilistic analysis of unstructured data, enabling models to recognize distinctions between pieces of content without human intervention.

LLMs undergo further tuning to perform specific tasks, such as interpreting questions and generating responses or translating text between languages.

How do LLMs Work?

Machine Learning and Deep Learning:

• LLMs are based on machine learning, a subset of AI where programs are trained on large datasets to identify features without human intervention. Deep learning, a type of machine learning, allows models to recognize distinctions autonomously, though some human fine-tuning is often necessary. Deep learning uses probability to learn, such as predicting the likelihood of characters appearing in text.

Neural Networks:

• LLMs use neural networks, which mimic the human brain’s structure with interconnected neurons. These networks have layers (input, output, and intermediate) that pass information if certain thresholds are met.

Transformer Models:

• Transformer models, a specific type of neural network, are excellent at understanding context, which is crucial for human language. They use self-attention to detect relationships within a sequence, making them adept at comprehending context and semantics.

Relations and Differences between LLMs and Transformers

Transformers:

• They are widely used in natural language processing (NLP), transformers excel at understanding word relationships in text. Unlike traditional models, they process sentences in parallel, improving efficiency. Examples include BERT, GPT, T5, and DialoGPT.
• Imagine a sentence: “The cat sat on the mat.” A transformer breaks down this sentence into smaller units called “tokens” (e.g., “The,” “cat,” “sat,” “on,” “the,” “mat,” and punctuation marks). Each token is represented as a vector, capturing its meaning and context. The transformer then learns to analyse the relationships between these tokens to understand the sentence’s overall meaning.

LLMs:

• A type of transformer trained on extensive text data, LLMs predict the next word in a sentence based on context. They are useful for tasks like auto-completion, translation, summarization, and creative writing. Examples include GPT-3.5 Turbo, GPT-4, BLOOM, LaMDA, MT-NLG, and LLaMA.
• For instance, if you provide the prompt “Once upon a time in a land far” an LLM can generate the next words as “away.” The LLM bases its predictions on the patterns and context it has learned during training on massive amounts of text. This makes LLMs useful for various applications, such as auto-completion, translation, summarization, and even creative writing.

we will be looking more in depth about LLM architecture in upcoming series of blogs

They Key Difference and Relationship between Transformers and LLMs:

While transformers are general models used for various tasks (e.g., language translation, speech recognition), LLMs are specifically designed for language modeling and text generation. Transformers provide the architecture that allows LLMs to capture contextual relationships and generate text.

What are Pipelines in Transformers?

There is a method in transformers which makes inferencing easy know as pipeline()

Pipelines in transformers provide an easy-to-use API for performing inference on various tasks, encapsulating processes like text cleaning, tokenization, and embedding. The pipeline() method from the transformers library simplifies implementation for tasks such as question-answering. This method supports default, existing, and custom models and tokenizers, facilitating the deployment of NLP tasks.

from transformers import pipeline

# To use a default model & tokenizer for a given task(e.g. question-answering)
pipeline("task-name")

# To use an existing model
pipeline("task-name", model="model_name")

# To use a custom model/tokenizer
pipeline('task-name', model='model name',tokenizer='tokenizer_name')

• LLMs use neural networks, which mimic the human brain’s structure with interconnected neurons. These networks have layers (input, output, and intermediate) that pass information if certain thresholds are met.
• The first line imports the pipeline function from the transformers library.
• The next three lines show how to use the pipeline function for different scenarios.
• The first scenario uses a default model and tokenizer for a given task, which is specified in the placeholder “task-name”.
• The second scenario uses an existing model, which is specified in the placeholder “model_name”, for the same task as in the first scenario.
• The third scenario uses a custom model and tokenizer, which are specified in the placeholders “model name” and “tokenizer_name”, respectively, for the same task as in the first two scenarios.
• Overall, the pipeline function allows for easy implementation of natural language processing tasks with various models and tokenizers.

What are Hugging Face Transformers?

Hugging Face Transformers is an open-source deep learning framework offering APIs and tools for downloading and tuning state-of-the-art pre-trained models. These models support tasks across NLP, computer vision, audio, and multi-modal applications. The framework includes pipelines that encode best practices and use default models for different tasks, making it easy to start with minimal training. Hugging Face provides a model hub with numerous pre-trained models and a library that supports their use and fine-tuning for various applications.

Hugging Face provides:

• A model hub containing many pre-trained models.
• The 🤗 Transformers library that supports the download and use of these models for NLP applications and fine-tuning. It is common to need both a tokenizer and a model for natural language processing tasks.
• 🤗 Transformers pipelines that have a simple interface for most natural language processing tasks.

ref:https://docs.databricks.com/en/machine-learning/train-model/huggingface/index.html

Conclusion

Conversational AI and large language models (LLMs) are transforming machine-human interactions. By leveraging technologies like speech-to-text, natural language understanding, and text-to-speech, conversational AI facilitates natural communication. LLMs, built on transformer models, use deep learning to understand and generate human language, proving useful in various applications from customer service to creative writing.

Transformers underpin these advancements, providing the architecture for LLMs to excel in language tasks. Pipelines in transformers and tools from Hugging Face have made implementing and fine-tuning these models easier.

As AI progresses, the integration of conversational AI and LLMs will continue to enhance interactions across industries, driving more sophisticated and natural engagements. The future of AI is promising, with these technologies leading the way.