Understanding Pre-trained Language Models
Last updated
Last updated
Pre-trained models are language models that have been trained on large amounts of textual data sourced from books, research papers and websites. Because of this extensive training, these models have gained an understanding of language's fundamental structure, grammar, syntax, and even some level of general knowledge. This allows them to perform a variety of natural language processing (NLP) tasks such as translation, text generation, summarization, and question answering.
Pretrained language models come in several configurations, each created for a specific task. They're built on top of the transformer architecture and typically fall into three categories:
An Encoder is the first part of the transformer architecture. Its role is to convert the input text into a numerical representation (Vectors) that the model can interpret. It’s basically a translator that transforms words into numbers and captures the meaning of each word in relation to others.
For an encoder-only model, the encoder processes the input text all at once to understand each word by examining its context within the sentence. Once this is complete, the encoder generates a set of vectors representing the meaning of the text. These vectors are then used for tasks like classification, question answering, or sentiment analysis. Here, the model doesn’t generate text; instead, it focuses on understanding the input and pulling out the important information.
A Decoder is the Second part of a transformer responsible for generating text based on a given input. Unlike the encoder, which focuses on understanding the input, the decoder is trained to predict the next word in a sequence, using the words before it.
Decoder-Only Models work step-by-step, generating one word at a time while considering the context of previous words. The use of self-attention captures the relationships between words in the sequence, This helps the model predict coherent and contextually relevant words. This process continues until the model generates a full response. Decoder-only models excel at tasks like text generation, conversational agents, and creative writing.
The Encoder-Decoder model combines the strengths of both the encoder and decoder. The encoder processes the input text, converting it into vectors that represent the meaning of the input, as described earlier. The decoder then takes these vectors and uses them to generate an output, such as translated text, summaries, or answers to questions.
The main difference between encoder-decoder models and decoder-only models lies in the way they handle inputs and outputs. While decoder-only models generate text by predicting the next word based on previous ones, encoder-decoder models process all of the input first and then pass this information to the decoder.
Pretrained models save both time and resources. Training a language model from scratch requires massive datasets and significant computational resources. Pretrained models allow us to utilize a model that has already mastered the basics of language. Rather than starting from zero, we fine-tune these models for specific tasks, needing less data and time. This is known as transfer learning.
While pretrained language models offer significant advantages in terms of efficiency and performance, they are not without their challenges.
Pretrained models are built to be highly generalizable, meaning they can apply the knowledge learned from one set of data to new tasks or situations. They can jump from one task to another with minimal fine-tuning. This is one of their greatest strengths, but this broad capability comes at a cost. Pretrained models are in many ways, a "jack of all trades, but master of none". While they can perform well on a variety of common tasks, they often lack the depth required for highly specialized or technical domains.
One of the most widely discussed issues with LLMs is their tendency to "hallucinate". Since these models rely on patterns learned from datasets rather than an inherent understanding of truth, they can sometimes generate plausible sounding but inaccurate or misleading information. This is particularly problematic when these models are used for tasks that require factual accuracy. In high-stakes scenarios this issue can lead to serious consequences if left unchecked.
While LLMs models excel at understanding language patterns, they sometimes struggle with deeper contextual understanding and complex reasoning. For instance, they might have difficulty following long conversations or keeping track of nuanced details over extended interactions. While they still can process basic logical constructs, real world problem solving often requires a deeper level of understanding and situational awareness; skills that pretrained models don’t always possess..
Pretrained models reflect the data they’re trained on, which means they can inadvertently reproduce or amplify biases present in that data. Whether it’s gender, racial, or cultural biases, these models can perpetuate harmful stereotypes. The ethical implications of such biases are significant, especially when these models are deployed in sensitive contexts like hiring, healthcare, or legal systems. Ensuring fairness and reducing bias in pretrained models is an ongoing challenge.
