Language models are computer programs that can learn and understand natural languages such as English, Spanish, Arabic, and so on. They are developed with machine learning techniques, massive datasets, and complex neural networks, among others. Over time, language models have evolved to become more sophisticated, with large language models (LLM) taking center stage in recent years. These large language models are based on deep learning and can handle vast amounts of unstructured data to perform a wide range of natural language processing tasks.
In this blog, we will take a closer look at large language models and understand their workings, applications, advantages, and limitations. Additionally, we will explore why human validation and post-editing remains critical in establishing the accuracy and reliability of these models.
What are Large Language Models?
Large language models are deep neural networks that can analyze and comprehend vast amounts of text data to generate natural language. They are pre-trained on massive language datasets, allowing them to capture language patterns and predict the likelihood of specific word sequences. GPT-4 and BERT are examples of large language models but there are over 150 others.
GPT-4 is a forth-generation generative language model capable of generating human-like text. It has hundreds of billions parameters, many times more than its predecessor, GPT-3. With a massive database of words and phrases, GPT-4 can predict and complete complex sentences with ease.
On the other hand, BERT (Bidirectional Encoder Representations from Transformers) is a language model that is well-suited for a wide range of natural language processing tasks, including question-answering, language translation, text classification, and sentiment analysis. This model is built from a neural network called a transformer, which can analyze text passages individually and as a whole.
How are Large Language Models Used?
Large language models have evolved to provide solutions for numerous natural language processing challenges. Here are some prominent examples of their usage:
- Machine Translation - Large language models are used to translate text from one language to another. These models use complex algorithms to understand the context, tone, and meaning of a text passage to translate it accurately.
- Text summarization - With the help of large language models, automatic text summarization can be achieved, where lengthy articles or reports can be condensed into smaller paragraphs without omitting critical information.
- Text completion - Large language models are used in predictive text systems such as auto-correct. They can predict and complete sentences based on the context of the input.
- Chatbots - Large language models can simulate human-like responses in chatbots, improving the overall customer experience in customer service.
- Sentiment Analysis - These models are capable of identifying the sentiment of a passage of text, helping businesses to gauge customer satisfaction and feedback.
Pros of Using Large Language Models
- High Accuracy - Large language models have a high accuracy rate in processing textual data. Moreover, the more data that is incorporated during training, the higher the accuracy and reliability of the model.
- Increased Efficiency - Large language models are equipped with high processing power, enabling them to handle vast amounts of data efficiently and quickly. This makes them ideal for applications such as automated summarization, classification, and information retrieval.
- Reduced Time Taken - Large language models can reduce the time taken to process text-based data. They can perform several tasks simultaneously, such as translation, language inference, and summarization, further increasing their efficiency.
- Human-like responses - Large language models can create responses that are similar to human responses, making them ideal for applications such as chatbots.
- Multilingual Capabilities - These models can handle multiple languages, making them versatile in industries or situations where cross-language communication is required.
Cons of Using Large Language Models
- Large Datasets - The size of the datasets used to train these models can make data processing slow and complex. This requires powerful processing infrastructure to enable the models to work effectively.
- Output Verification - Additionally, the output of these models can sometimes be unreliable and require human validation and post-editing to ensure accuracy.
- Limited Interpretation - As models become larger and more complex, they become more difficult to interpret. Understanding the underlying features that a large language model has learned can be challenging.
- Biases - Language models can replicate biases that exist within the data used to create them, affecting the output that the models create.
- Inability to Interpret Nuances - Some subtle semantic nuances in language may be difficult for language models to recognize, leading to incorrect analysis.
Examples of Large Language Models
- GPT-4 -The most infamous of LMMs, this model has the ability to generate human-like text and achieve human-like performance across natural language processing (NLP) tasks such as language inference, summarization, and question-answering.
- BERT - A pre-trained language model developed by Google that can effectively execute a variety of NLP tasks, including text summarization, sentiment analysis, text classification, and question-answering.
- XLNet - XLNet is another language model based on the Transformer architecture that uses unsupervised pre-training to generate text. It offers better performance on some tasks than BERT and is particularly useful for language tasks that require context.
- T5 (Text-to-Text Transfer Transformer) - T5 is a language model developed by Google that can be fine-tuned for a variety of natural language processing (NLP) tasks such as question answering, summarization, and language translation.
- RoBERTa (Robustly Optimized BERT approach) - RoBERTa is an extension of BERT that was optimized for pre-training on large corpora of texts. It has been shown to perform better on a wide range of NLP tasks than BERT.
- DistilBERT - DistilBERT is a smaller and faster variation of BERT that was developed by Hugging Face. It has fewer parameters than BERT, which makes it easier to use on smaller devices for real-time text processing.
- Wide & Deep (WnD) - A hybrid architecture model that combines deep learning with a machine learning model. This model is used primarily for personalized recommendations.
Why Human Validation and Post-Editing is Important
Large language models are useful in natural language processing tasks, but they have their limitations. Although large language models have made significant advancements in language processing, the outputs they produce are not always accurate or free of human error. This is where human validation and post-editing come in.
Human validation is the process of reviewing model output and verifying its accuracy to ensure that the model's output is consistent with human expectations. With this process, human experts can check the error rate, correctness, or linguistic rules of the model's output.
Post-editing, on the other hand, is the process of improving the output generated by a machine translation or other language models with the help of human experts. It might involve correcting grammatical errors, refining vocabulary, or improving phrasing to make the text sound more human-like.
Large language models are transforming the natural language processing landscape and have the potential to create new possibilities in NLP-based industries. They are efficient and highly accurate, making them a favorable alternative to traditional language models. However, they also have their limitations, such as reliance on massive datasets for training and the potential to replicate human biases. Additionally, human validation and post-editing are necessary to establish the accuracy and reliability of these models.
In conclusion, while large language models are powerful and offer numerous advantages, it is important to ensure that their output is accurate and human-like. This requires collaboration between linguists and technology experts, and it will remain a critical aspect of language model development and use for the foreseeable future.