Enterprise AI - Demystifying the mechanics of AI Models

For enterprises eager to harness the power of artificial intelligence models (AI models), gaining insights into their inner workings is crucial. Specifically, transformer-based language models (LLMs) employ encoders, decoders, embeddings and fine tuning / RAG techniques in distinctive ways. This article aims to demystify these concepts, providing a comprehensive understanding for enterprises venturing into the realm of AI.

Distinguishing Encoders from Decoders:

A prevalent architecture in many AI models is the encoder-decoder configuration, comprising two integral components:

• Encoder: This component transforms input data, such as text, into a fundamental numeric representation known as encoding.
• Decoder: Responsible for converting the encoding into a novel representation by passing it through multiple layers. In applications like machine translation, the encoder reads the source text, generating a generic encoding that the decoder subsequently transforms into the target language text

In contrast, decoder-only models like GPT-3/4 or Gemini or Falcon amalgamate encoding and decoding within a single component. The initial decoder layer encodes the input, with subsequent layers effecting further transformations. Despite this difference, both approaches involve encoding and subsequent deep processing.

The unique advantage of decoder-only models is that they can generate text quickly and efficiently because they don't need to spend time understanding the input context. They are good at tasks where the output depends on what has been generated so far are emerging technology in the field of generative ai or GenAI.

Embeddings – Structured Representations of Meaning:

An embedding, a specific type of encoding, bestows the numeric representation with structured and meaningful properties. For instance, embeddings facilitate the mapping of similar words, such as "happy", "cheerful" and "joyful," to comparable vector representations, encapsulating semantic meaning within the encoding.

Transformative models like GPT generate embeddings by subjecting text to multiple layers, each contributing to the creation of a new vector representation. The final output embedding encapsulates the cumulative semantic meaning derived from all layers of transformations applied to the original text.

Few Insights When to Employ Encoders versus Embeddings:

Choosing between encoders and embeddings depends on the specific needs of an AI application. Here are some guidelines:

Encoders prove beneficial for:

• Machine translation – Constructing representations of complete source sentences before translation.
• Text classification – Encoding entire documents, such as news articles, to discern topics.
• Anomaly detection – Analyzing sequence data, like credit card transactions, to identify outliers.

Embeddings suffice for:

• Recommendation / Search Systems – Utilizing embeddings for queries or product descriptions provides semantics for matching.
• Chatbots – The embedding of the current user utterance contains adequate context for generating the next bot response.
• Keyword spotting – Detecting keywords in audio through embeddings, without requiring sequence information.

With latest advancements in Decoder-Only Models Recent strides in technology have empowered decoder-only models like Gemini or Falcon or GPT-3/4 or Open-Source models (Mistral) to undertake tasks that previously necessitated encoders:

• Increased context lengths – LLMs supports extensive tokens of context, enabling effective conditioning on source texts for translation and other applications.
• Chunked processing – Handling long sequences, such as documents, by processing them over multiple passes of shorter chunks.
• Increased accuracy - Latest advancements with GPT4 or other LLMs expected to show even greater improvements in language understanding and prediction. This could lead to more accurate text completion, summarization, and response generation, enabling more efficient communication and enhanced user experiences.
• Reduced bias - One of the major concerns with AI models like GPT-3 has been the presence of biases in their outputs. GPT-4 focuses on addressing this issue by refining training data and algorithms to minimize the presence of discriminatory or offensive content. For example, prompts with “give me a controversial take on…” will mostly be ignored.?

Consequently, for numerous applications, enterprises can now opt for efficient decoder-only models instead of traditional encoder-decoder configurations.

RAG - (Retrieval-Augmented Generation) :

Within the domain of experimental Large Language Models (LLMs), crafting an engaging LLMs Minimum Viable Product (MVP) may seem relatively uncomplicated. However, the transition to achieving production-level performance poses a formidable challenge such as "hallucinations with LLMs response", particularly when tasked with constructing a robust Retrieval-Augmented Generation (RAG) pipeline geared towards in-context learning.

The beauty of RAG lies in its ability to enable a language model to draw upon and leverage your own data to generate responses. While base models are traditionally trained on specific, point-in-time data, ensuring their effectiveness in performing tasks and adapting to the desired domain, they can struggle when faced with newer or current data.

This is where techniques like fine-tuning and RAG can supplement the base model. Fine-tuning can be effective for continuous domain adaptation, enhancing model quality but often at increased costs. RAG, on the other hand, allows for the utilization of the same model as a reasoning engine over new data, empowering businesses to use LLMs more efficiently without the need for expensive fine-tuning.

Key Considerations for Enterprise AI:

To navigate the complexities of AI system development, enterprises should heed the following key takeaways:

• Encoder-decoder models segregate encoding and decoding steps, while decoder-only models amalgamate them.
• Embeddings capture meaning by transforming input through multiple layers.
• Opt for encoders when sequence order is paramount, and choose embeddings when localized semantics suffice.
• Recent advancements enable decoder-only models to handle tasks that once required encoders.
• Carefully assess which architecture aligns with your use case based on sequence requirements and semantic considerations.
• Wisely chooses fine tuning vs RAG (Retrieval-Augmented Generation) pipelines to production-level performance of your MVP;

By comprehending the intricacies of encoders, decoders, the creation of embeddings and fine tuning / RAG (Retrieval-Augmented Generation) enterprises can forge ahead in developing more advanced and scalable AI solutions.

How to succeed enterprise AI, checkout article - Inner voice on Enterprise Gen AI

Like To Learn More:

• NLP at MIT
• An Overview of Encoding Models in NLP
• Gartner Gen AI Insights