Vedansh Shrivastava — Published On December 26, 2022 and Last Modified On December 30th, 2022
chatbot ChatGPT Intermediate Listicle NLP


There have been many recent advances in natural language processing (NLP), including improvements in language models, better representation of the linguistic structure, advancements in machine translation, increased use of deep learning, and greater use of transfer learning. These advances have significantly improved a wide range of NLP tasks, including language modeling, machine translation, and sentiment analysis.

The year 2022 has been the most fruitful for NLP in recent times. Numerous new models have been created, and a great deal of updating has occurred. I have compiled a list of the top 10 NLP advancements that are the most effective and popular of all the breakthroughs this year.

Improving Text Representation

Accurate representation of text is necessary as it allows the machine to understand the meaning and intent of the text and allows us to perform various tasks such as text classification, language translation, and text generation.

As we know to input textual data into the NLP models, we need to convert that textual data to their embeddings. And the results of these models depend on these embeddings only.

There has been huge advancement over the years to find the vector representation of the textual data, starting from using the frequency of the words to find their vectorized representation to getting word embeddings while keeping the intent and meaning of textual data in mind; these advancements have led to significant improvements in various NLP tasks.

Some of the key milestones in the advancement of word embedding include Word2vec, Glove, Fast text, Elmo, Bert, and a few more.

The year 2022 was also the year of significant improvements and advancements in a word embedding. Here are some of the NLP models that were launched this year.

Data2Vec 2.0

Data2Vec2.0 is an updated release for the model Data2vec. Data2vec is a self-supervised learning algorithm, meaning it can learn from vision, text, and speech without needing explicit labels. Self-supervised learning algorithms learn by using the inherent structure of the data itself.

Data2Vec2.0 has shown tremendous results for tasks like text understanding image segmentation and speech translation task.

Similar to the original data2vec algorithm, data2vec 2.0 predicts contextualized representations of the data, meaning they take the entire training data into account.

Data2Vec2.0 is an improved version then all its predecessors as it is way faster than any other model and does not compromise accuracy.

For speech, the test was done on the LibriSpeech speech recognition benchmark, where it performed more than 11 times faster than wav2vec 2.0 with similar accuracy. For natural language processing (NLP), evaluation was done on the General Language Understanding Evaluation (GLUE) benchmark, which achieved the same accuracy as RoBERTa and BERT.

The architecture of Data2Vec 2.0


To know more about the topic, refer to this link

New and Improved Embedding Model

Text-embedding-ada-002 was recently launched by openAI. It has outperformed all the previous embedding models launched by openAI.

Text-embedding-ada-002 is trained using a supervised learning approach, which means that it is trained on a labeled dataset that consists of text input and corresponding targets.

The model uses a transformer-based architecture designed to process sequential data such as text. The transformer architecture allows the model to effectively capture the relationships between words and phrases in the text and generate embeddings that accurately reflect the meaning of the input.

The new model, text-embedding-ada-002, replaces five separate models for text search, text similarity, and code search and is priced way lower than all the previous models.

The context length of the new model is increased, which makes it more convenient to work with large documents, while the embedding size of the new model is decreased, making it more cost-effective.

Image and Video Generation

Technology has made it possible to generate images and videos based on a basic textual description of a situation or image. Image and video generation in NLP is a rapidly developing field with much research and a lot of advancement in this field yet to come. Some of the key applications include creating content, advertising, and creating realistic images.

Here are some of the key advancements that took place this year


Imagen, developed by Google and launched in 2022, is a text-to-image diffusion model. It takes in a description of an image and produces realistic images.

Diffusion models are generative models that produce high-resolution images. These models work in two steps. In the first step, some random gaussian noises are added to the image and then in the second step, the model learns to reverse the process by removing the noise, thereby generating new data.

Imagen encodes the text into encodings and then uses the diffusion model to generate an image. A series of diffusion models are used to produce high-resolution images.

It is a really interesting technology as you can visualize your creative thinking just by describing an image and generating whatever you want in moments.

Now let me show you guys the output image I got using a certain text

Text: A marble statue of a Koala DJ in front of a marble statue of a turntable. The Koala wears large marble headphones.

Output Image:

Output Image of a Koala DJ by Imagen


I know that was something really fascinating, Right!!. To know more about the model, refer to this link


DreamFusion, developed by Google in 2022, can generate 3D objects based on text input.

The 3D objects created are of high quality and are exportable. They can be further processed in common 3D tools.

Video of some 3D images produced by DreamFusion


The 3D model created is based on 2D images from the generative image model Imagen so you also don’t need any 3D training data for the model.

Interesting, Right!!, Now go and refer to this link to learn more about the model.


DALL-E2 is an AI system developed by OpenAI and launched in 2022 that can create realistic images and art based on textual descriptions.

We have already seen the same technologies, but this system is too worth exploring and spending some time. I found DALL-E2 as one of the best models present, which works on image generation.

It uses a GPT-3 modified to generate images and is trained on millions of images from over the internet.

DALL-E uses NLP techniques to understand the meaning of the input text and computer vision techniques to generate the image. It is trained on a large dataset of images and their associated textual descriptions, which allows it to learn the relationships between words and visual features. DALL-E can generate coherent images with the input text by learning these relationships.

Let me show you how DALL-E2 works

Input text – Teddy bears

Output Image-

NLPImage of Teddy bears produced by DALL-E2


Here is the link to the research paper if you are interested to read in detail here.

Conversational Agents

NLP has made it possible for humans to interact with computer applications the way they would with other humans. Most E-commerce applications, food ordering platforms, and Delivery platforms are using chatbots for their users. They can be integrated into websites, messaging apps, and other platforms to allow users to interact with them using natural language. Recent advancements in NLP have enabled the creation of more advanced and realistic conversational agents.

Here are some top Conversational models launched in 2022

LaMDA: Towards Safe, Grounded, and High-Quality Dialog Models for Everything

LaMDA (Language Model for Dialogue and Answering), developed by Google, is a language model designed for answering and dialog tasks.

This model can be used in various ways, such as chatbots, customer service, Virtual Assistants, etc.

One of the key features of LaMDA is its ability to generate coherent responses grounded in the input text. This is achieved through the use of a transformer-based language model that is trained on a large dataset of human conversations. The model is able to understand the context of the conversation and generate appropriate responses based on the content of the input text.

LaMDA can generate high-quality responses on a wide variety of topics and open-ended questions.

The developers have also kept in mind the sanity of responses generated by the model, and it avoids generating offensive and biased content.

I’m sure you guys would want to see a demo of this amazing bot. So here it is!

Conversation with LaMDA


For in-depth knowledge, refer to the link here


ChatGPT, developed by OpenAI, was recently released in late November and is one most trending and viral AI product launched in 2022. Almost all data professionals are trying and researching this amazing chatbot.

ChatGPT is based on the GPT-3 (Generative Pre-trained Transformer 3) language model, a large, transformer-based language model trained on a massive dataset of human-generated text.

ChatGPT can generate coherent responses and can, understand the context of the conversation, and generate appropriate responses based on the content of the input text.

It is designed to carry conversations with people. Some of its features include answering follow-up questions for various topics.

The accuracy and the quality of the responses generated by the model are incomparable to any other chatbot.

Here is the demo of how ChatGPT works


Conversation by chatGPT

Refer to this link to learn more about the model here

Automatic Speech Recognition

We can interact with our devices, home appliances, Speakers, and Phones due to the presence of virtual assistants like Siri, Alexa, Google Assistant, etc. Technology allows us to talk to devices that interpret what we’re saying to respond to our questions or command. Here are some improved Automatic speech recognition models launched in 2022 that took the advancement in technology to the next level


Whisper, developed by OpenAI, is a technology that helps in the conversion of Speech to text.

It has multiple uses like Virtual assistants, voice recognition software, etc. Moreover, it enables transcription in multiple languages and translation from those languages into English.

Whisper is trained on 680,000 hours of multilingual and multitask data collected from the web. The use of a large and diverse dataset has led to increased accuracy of the model.

Whisper uses encoder-decoder architecture in which the input audio is split into chunks of 30 seconds, converted into a log-Mel spectrogram, and then passed into an encoder. A decoder is trained to predict the corresponding text caption.

Whisper can be trained on large datasets of speech and transcription pairs to improve its accuracy and adapt to different accents, languages, and speaking styles.


The architecture of Whisper


Transfer Learning in NLP

Transfer learning is a go-to approach for building high-performance models. In transfer learning, the model is trained on large and general datasets and is fine-tuned for our related task. It has been widely used in natural language processing (NLP) to improve models’ performance on almost each and every task. There has been significant research in 2022 around improving the transfer learning techniques. We will discuss the top 2 breakthroughs in this area now.

Zero-Shot Text Classification with Self-Training

As a result of recent developments in big pre-trained language models, the importance of zero-shot text categorization has increased.

Particularly, zero-shot classifiers developed using natural language inference datasets have gained popularity due to their promising outcomes and ready availability.

The self-training approach only requires the class names and an unlabeled dataset without needing domain expertise. Fine-tuning the zero-shot classifier on its most confident predictions leads to significant performance on a wide range of text classification tasks.

You can read more about this approach in this conference paper.

Improving In-Context Few-Shot Learning via Self-Supervised Training

In-context few-shot learning refers to learning a new task using only a few examples within the context of a larger, related task. One way to improve the performance of in-context few-shot learning is through the use of self-supervised training.

Self-supervised learning involves training a model on a task using only input data and without explicit human-provided labels. The goal is to learn meaningful representations of the input data that can be used for downstream tasks.

In the context of in-context few-shot learning, self-supervised training can be used to pre-train a model on a related task, such as image classification or language translation, to learn useful data representations. This pre-trained model can then be fine-tuned on the few-shot learning task using only a few labeled examples.

Read in detail about the approach in this paper.


In conclusion, the advancements in natural language processing (NLP) have been significant in recent years, leading to significant improvements in the ability of computers to understand and generate human language. These advancements have been made possible by developing more sophisticated algorithms and the availability of large amounts of data and computational resources. NLP has a wide range of applications, including language translation, text classification, sentiment analysis, and chatbot development, and it has the potential to revolutionize how we interact with computers and access information. As NLP technology continues to improve, we can expect to see even more exciting developments in the future.

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