From medicine to automobiles and finance to eCommerce, Machine Learning models can classify elements just by recognizing images or voices. The credit can be attributed to advanced data labeling techniques, a critical process in developing machine learning (ML) models.  

The automated data labeling process utilizes supervised learning models that are trained on pre-defined labeled data to comprehend and process given data more accurately. It allows the model, or neural network, to learn how to make informed decisions that it believes ought to produce the desired output. 

These data labels help AI to identify objects or elements along with other crucial information like images, text, audio, or video. These models are trained on vast datasets with unsupervised or self-supervised learning. It can produce human-like content without or with minimal manual intervention. 

In this blog, let us understand cutting-edge data labeling techniques and explore hybrid models and LLMs.

Why There is a Need to Shift to Traditional Data Labeling Practices? 

Traditional data labeling is often a manual, tedious, and time-consuming process. Not to mention that there are chances that errors could happen, and when it comes to training AI and ML models, one minor mistake could turn out to be an outstanding expense in the long run.

For instance, in internal labeling, once the company labels the data, it significantly ensures labeling accuracy and data security, as no third party has shared the data. Although this technique is useful, it might be costly and time-consuming as it requires highly paid professionals.

Another traditional data labeling practice is external labeling, where the company outsources the labeling to vendors who aren’t integral to the company. This implies that resource acquisition is more flexible, faster, and cheaper with reliable vendors, and minimum client management is required.

Although this technique is also very useful, it has some disadvantages. The outsourced vendors need to create a large number of labels as they may not completely understand the company’s specific needs. In addition, external labeling isn’t secure, as there are high chances of data breaches.

Hence, it is best to switch to modern and cutting-edge practices, like utilizing LLM for data labeling. Let’s discuss more about automated data labeling practices.

Introduction of Automated Data Labeling: Hybrid Labeling Model and LLMs

Automated data labeling solutions use rule-based algorithms and predefined guidelines to label raw data automatically. Machine learning models make it feasible to automate the process of assigning labels to data with high precision. 

This process begins with training a model on high-quality data and feeding fresh, unlabelled data. With time, the model will refine its accuracy and achieve a higher level of precision in labeling data as compared to manual methods. 

The algorithms can easily label extensive datasets, making them cost-effective and scalable for projects with extensive data requirements. Automation can establish consistency and efficiency, but being in its initial stages, it can still struggle with complex and nuanced labeling objectives. 

Hence, human oversight and manual review will still be required to ensure a high level of accuracy and reliability of labels.

How LLMs Can Be Utilized For Data Labeling? 

If you are wondering how LLMs can be utilized for data labeling, then here’s a list of tasks that LLMs can complete efficiently: 

• Text Classification: LLMs can classify text documents into predefined categories or assign them labels if they are fine-tuned on specific datasets. Data scientists work creating text classifiers that can automatically label text data with high accuracy.
• Named Entity Recognition (NER): The large language models can be fine-tuned for NER tasks, which helps identify and label entities like names, dates, locations, and more in unstructured text data.
• Sentiment Analysis: LLMs can also be trained to determine the sentiment of a particular piece of text and categorize it as positive, neutral, or negative. This is essential for customer reviews, social media sentiment analysis, and others. 
• Text Generation: LLMs can also generate labels or summaries for the text data. With Gen-AI capabilities, they can simplify the labeling process by creating short and up-to-the-mark product descriptions in eCommerce datasets. 
• Question Answering: LLMs can also be fine-tuned to generate labels automatically by asking questions about the data’s content. 
• Language Translation: Nowadays, LLMs support multilingual capabilities, which means they can also assist in labeling multilingual datasets. 

However, there are some things to consider if you are thinking of using LLMs as an annotator. First of all is the prompt techniques that you will be using to fetch the output. There are zero or few-shot prompt techniques available, but which one will be effective?

Zero-shot prompting requires the LLM to answer without giving an example in the prompt. For instance, what is the sentiment of “I saw a Gecko”?

On the other hand, few-shot prompting requires providing some examples to the LLM before asking the question. For instance:

The sentiment of “I love elephants” is positive. 

The sentiment of “I don’t like snakes” is negative. 

What is the sentiment of “I saw a Gecko”? 

Now, some resort to few-shot prompting, while some say that zero-prompting works for them. So, it will entirely depend upon your use case and model to figure out which technique works for you. 

Another factor to consider is the model’s sensitivity to changes in the prompt. A slight change in the prompt’s structure can significantly affect the response. Therefore, it becomes essential to understand the extent to which the response differs from the degree of change in the prompt’s structure. 

There is one best way to analyze this. Ask the expert to generate or provide the initial prompt. Now, using the LLM, generate four more prompts with similar meanings and ask the LLM model to average the result of all five prompts. 

Automated Data Labeling Techniques: Training LLMs

If you are starting from scratch and thinking of training an LLM, then you will have to hire a generative AI expert who can assist you in navigating the process. If you are interested in hiring skilled developers at an affordable expense, then you should try hiring remote personnel from the LATAM region. This region is emerging as a hub of skilled and talented developers with hands-on experience in working with advanced data labeling techniques, like hybrid labeling models. 

The hybrid labeling model combines manual annotations with automated systems and is much more efficient in accuracy than traditional labeling. This approach includes three primary methods: semi-supervised learning, active learning, and weak supervision. They can be used as separate techniques or in combination with one another for maximal outcomes. Let’s discuss them in more detail:

• Semi-supervised learning (SSL)

Semi-supervised learning (SSL) uses a small set of labeled data alongside a larger set of unlabeled data. This technique is cost-effective and helps improve model performance by leveraging the unlabeled data. In SSL, a model uses labeled data to make predictions on the unlabeled data, then retrains itself with the predictions it is most confident about. This is known as self-training. Another approach, graph-based methods, uses data simplicity to propagate labels.

SSL is widely used in areas like image recognition, speech processing, and natural language processing (NLP). For instance, Meta used SSL to improve its speech recognition models by training on 100 hours of labeled data and adding 500 hours of unlabeled data.

• Weak Supervision

Weak supervision trains models using imperfect, noisy, or approximate labels from various sources. It allows models to learn from large amounts of weak supervisory data, reducing the need for high-quality labels. Weak supervision relies on techniques like data programming, which combines noisy labels from different sources, adjusting for their accuracy and correlation to create a reliable training set.

This method is beneficial in domains like medical image analysis, where expert annotations are expensive. It is also useful in web data extraction, where manual labeling is totally unrealizable given the dimension of available data.

• Active Learning

Active learning is a form of SSL where the model selects the most informative data points for human annotators to label. In active learning, the model focuses on data points it is most uncertain about. The methods include:

• Uncertainty sampling (the model asks for labels where it is least confident)
• Query by committee (several models vote on labels, and uncertain points are sent for labeling)
• Expected model change (Focuses on data points that would significantly impact the model’s parameters.

Active learning is used in tasks like medical image classification. For instance, in pneumonia detection, a model selects uncertain X-rays for radiologists to label, improving its accuracy over time.

Benefits of Automated Data Labeling

Seamless integration of LLM with data labeling can optimize and streamline workflow, offering several benefits:

• It significantly reduces the time spent on manual labeling. LLMs can handle vast data labeling tasks from simple classification of text, images, and audio to complicated entity recognition, thus qualifying as an all-purpose automated data labeling tool.
• In the case of data, LLM can handle huge amounts of data sets in just a few minutes, compared to manual data labeling. The best part is that it’s 20 times faster and 7 times cheaper than hiring skilled human annotators.
• By leveraging and automating the LLM data labeling process, companies can easily cut labor costs and boost their labeling capacity without compromising quality.

However, to ensure seamless integration, you will have to hire suitable developers and engineers who can help you achieve your business objectives. 

Wrapping Up

Consequently, in this rapid technological advancement, data labeling isn’t left behind and continues to evolve. It will be possible to maximize the scalability and efficiency of labeling along with bias in the labeled data with applications driven by AI.

If you are looking to hire a generative AI professional expert to assist you with automated data labeling, then Hyqoo’s Talent AI Cloud can help to fill up the vacant position in your team within 2-3 days. Visit our website today and describe your requirements, and we will provide you with the best talent with several years of hands-on experience in Gen AI and data labelling.