Easy Explanation on Training Language Models to Follow Instructions with Human Feedback Major impact via ChatGPT and Popularized RLHF

Welcome to our blog on “Easy explanation on Training various  Language models to Follow Human’s instructions with feedback and its huge impact on  ChatGPT and popularised RLHF”.

Language Models had been recently improved in the recent years. They can easily generate response and compel text from human input prompts. They are used in the field of creativity, used to generate code for programming languages and to give us the useful informative text which is most correct and accurate.

First of all, we have to see what is a Language model and how it is used in a simple manner.

Language Model

Language model is the combinational use of various statistical and probability techniques to determine or predict the sequence of words that occur in a given sentence. It mainly generates text as an output.

It is mainly used in Natural Language Processing applications, Machine Translation and Questions and answering.

 Language Models analyse the word probability of the given text data by using the algorithm fed to it and establishes the rule for context in Natural Language. They can accurately produce text by using these algorithms. World’s biggest companies like Twitter, Facebook, Google etc, use these Language models in their Web platforms.

There are some of the models of the models of the Language models like Unigram Model, N Gram Model, Bidirectional, Exponential, Continuous Space etc.

Now, we have to see the importance of the Natural Language modelling

 IMPORTANCE OF LANGUAGE MODELS

Language Models are the backbone of the Natural Language Processing. They have achieved greater importance due to their applications in certain tasks.

 a) Machine Translation

Machine translation is used for the  translation of one language to another by a machine. Google Translate and Microsoft Translator are some of the developments of this model . Recently SDL Government is another language developed by the US Government which is used to translate foreign social media feeds in real time for the U.S. government.

 b) Parsing

It is used for the analysis of any string type of data or sentence that was written according to formal grammar and syntax rules. In language modeling, it establishes the relationship between the words. Also,  Spell checking applications like Grammarly  use language modeling and parsing.

 c) Speech Recognition

Speech Recognition makes a  machine being able to process and generate  speech audio. This is commonly used by voice assistants like Siri, Google Now, ALICE and Alexa.

 Now, we will see how these Training Language Models are used in ChatGPT and RLHF and how do they generate feedback by using their algorithms.

 Language Models applications in ChatGPT

Language Models are specially designed to understand, interpret and generate human like text and based on the given data input. Large Language Models are mainly used for this types of tasks. LLM acquires a complex and comprehensive knowledge of data analysis and contextual data, allowing them to excel in natural language processing tasks and Machine Translation tasks. 

 LLMs utilize deep learning techniques, such as transformer architectures, to capture complex patterns and relationships within the given textual data. These models, like GPT-3 and GPT- 4, BERT have been proven to be remarkably excel in tasks like text classification, summarization, translation, and question-answering. 

 Now, we will see RLHF concept and see its multiple model training capabilities.

 Reinforced Learning from Human Feedback

 Reinforcement learning from Human Feedback (also referenced as RLHF Model) is a challenging concept. It consists of  multiple-model training process and different stages of deployment. We will see the certain steps involved in it.

1. Pretraining a Language Model

2. Gathering data and training a reward model

3. Tuning the Language Model with Reinforced Learning

These three steps play a key role in deploying a language model using RLHF.

 While these techniques are extremely powerful  and impactful and have limitations. The models, while better, can still output harmful or factually inaccurate text without any uncertainty. This imperfection represents a long-term challenge and motivation for RLHF – operating in an inherently human problem domain means there will never be a clear final line to cross for the model to be labeled as  complete.

When deploying a system using RLHF, gathering the data is quite expensive due to the direct integration of other human workers outside the training loop. RLHF performance is only as good as the quality of its human annotations, which takes on two varieties: human-generated text, such as fine-tuning the initial LM in Instruct GPT, and labels of human preferences between model outputs.

One large cost of the feedback portion of fine-tuning the Larger Language Model  policy is that every generated piece of text from the policy needs to be evaluated on the reward model and it is tuned and deployed in a perfect manner. 

Hence, we have seen how Trained Language Models are used to run applications like ChatGPT and essential role of RLHF Concept in deploying the Language models and tuning for their perfect use in the Language model tasks.

So, friends you had seen about Training Language Models, their applications in ChatGPT and RLHF.

Now, you will exactly know what is Training Language model and its applications in various tasks like ChatGPT and You had also learnt about RLHF Concept and its contribution in Language models deployment.

I hope that you all had learnt something new in this blog and I feel it is useful to all you at some point of time in your Data Science career.

All the Best and Please, don’t forget to share your thoughts and opinions in the comment session.