by Vidya Peddinti
Understanding Large Language Models (LLMs)
Large Language Models (LLMs) are algorithms or systems. They aid machines in comprehending and producing text that resembles that of humans. They were created with the explicit intent of learning, comprehending, producing, generating, summarizing, and predicting new information from extremely big data sets. They are the most extensively used tools in the field of natural language processing and aid in our ability to operate more quickly and efficiently. LLMs are a type of AI which uses deep learning techniques, supervised learning, and ensemble learning which aggregates the predictions from multiple Machine Learning Algorithms that are more accurate. Some of the well known LLMs include ChatGPT, GPT-3, GPT-3.5 Turbo, PaLM, Ernie-3.0, and LaMDA.
General Architecture
General architecture of LLMs have multiple layers of neural networks (Recurrent layers, feedforward layers, embedding layers, attention layers etc). These all layers work together to take input through a prompt and generate output.
Here is a brief explanation of how the layers work:
- Embedding layer :
It takes input, converts each letter of the input into high-dimensional vector representation. Then it takes the semantic and syntactic information from the words, and helps the model to understand the context.
- Feed forward layer:
It takes the input embeddings, applies non – linear transformations, and helps to learn higher level abstraction from the input.
- Recurrent layer :
It interprets information from input in sequence, has hidden layers which are updated after each step, and helps the model to identify the dependency between words and sentence.
- Attention layer:
It helps the model to focus selectively on different parts of the input, helps in finding most relevant and accurate predictions.
Generative AI can help to provide hyper-personalized learning insights to students. LLMs can be helpful in enhancing the process of learning, teaching, management, data analysis, quality education or any domain related to EdTech companies. AI can provide personalized learning experiences with instant feedback to have potential learning outcomes.
Applications
Let us see some of the applications in the EdTech sector and the benefits of LLMs.
Language Tasks
As chatbots can converse with us like real people, LLMs can be utilized for tasks related to learning new languages and enhancing our speaking and writing abilities.
- Translation: Translating text from one language to another.
- Improving Language skills: Finding a word in different languages, synonyms, antonyms etc will help in improving language skills. This also improves the fluency of speaking in a particular language.
- Grammar: When we type some text and ask the model to correct it, then it will correct the errors and will also give the right answer.
- Conversation practice: LLM GPTs are designed in such a way that we can have conversation with them through text. They remember a certain amount of our previous conversations. So based on that they respond to the user. This will help in talking in different languages with the model.
Questions Answering
On whatever contents the LLM is trained, it will have a clear idea of all the data. It will analyze what the user is searching for and generate related output. So it can answer the questions about a specific thing like a subject or a problem and explain in detail. The question could be anything related to a particular content.
Content Creation
Everyday new things arise and LLMs can be the best tools to make our work easy and effective to create content, generate information etc
- Writing: Gives content on the topics it has been trained. It can write essays etc
- Editing: It can edit any paragraph and output the correct one.
- Recreation: It can read any existing text that we give it to and then it can create the same text in a different way.
- Summarization: It summarizes any paragraph(s), PDFs, Excel, etc.
- Generation: Stories/dialogues/poems/news articles/Novels with particular themes
- Code Generation: Generates codes in different languages like Python, Java etc. and explains it
- Social Media Content: To create content for its social media posts, personal blogs, video scripts
- Email writings: It can also write emails for us.
- Presentations: It can create own presentations for us
Data Analysis
LLMs can analyze our interactions, text data in any files etc.
- It can analyze any Excel sheets data, PDFs, or any other content in text
- Analyze financial data of the company
- Generates budgets
- Offers investment advices
- Helps also the student analyze the financial data and keep track of his dues, payments
Personalized Recommendations/Tasks
LLMs can understand and remember our conversations. Based on the way we interact with it, search results, questions, and queries it can give us recommendations.
- Based on the current level of understanding, it can generate questions for students to answer
- Generating riddles to understanding a complex things
- It can also generate interactive questions/tests
- Based on the previous questions, recommends new courses to learn /personalized learning plan
- Recommends personalized videos, articles, news feeds
- Based on the goals of the student, it can create or recommend personalized courses /workout plan/nutrition plan /articles/exercises to improve knowledge/sports plan/exploring their interests
- Creates personalized schedules/time table/to-do list/study plan based on the availability/tasks and performances of student
- Personalized recommendations like goal setting, time management
- Provide resources for studying a particular subject
- Gives suggestions to read books based on the searching results
Library maintenance
In a Library, Large number of check-ins and check-outs happen daily. Not only that but a lot of people come for paper reading, studying, working etc. LLMs can be helpful in this area to
- Takes care of the check-ins and check-outs of the books
- Assisting in finding books in Library
- Give suggestions to read books based on the searching results/ previous check-out books
- Booking meeting rooms / focus rooms / discussion rooms for students
Assessment and Evaluation
Not only students but LLMs can be helpful to teachers also in grading the test papers or in automated assessments.
- Creates effective assessments
- Evaluates the answers
- Gives suggestions after each evaluation to where to be improved
- Faster and accurate feedbacks
- To create automated grading systems
To conclude, We have seen the significant challenges that chatGPT brought to the EdTech sectors. The impact of usage of Generative AI has become more noticeable since ChatGPT arrival than before. Either we should embrace the capabilities of these LLMs or risk falling behind. LLMs can restructure the EdTech sector and reshape society. At the end we should maintain the overall quality of the generated content, the accuracy and the safety along with the consideration of ethical values.
References
https://www.hellotars.com/blog/llm-ai-applications-and-use-cases/
https://vectara.com/large-language-models-use-cases/
https://www.techtarget.com/whatis/definition/large-language-model-LLM
https://cellstrat.medium.com/real-world-use-cases-for-large-language-models-llms-d71c3a577bf2
https://paperswithcode.com/paper/multilingual-machine-translation-with-large
https://arxiv.org/abs/2304.04675
https://towardsdatascience.com/how-to-detect-and-translate-languages-for-nlp-project-dfd52af0c3b5
https://huggingface.co/blog/starcoder
https://metatext.io/datasets-list/translation-task
https://paperswithcode.com/datasets?task=text-summarization
https://huggingface.co/datasets/bigcode/the-stack
https://paperswithcode.com/task/abstractive-text-summarization/codeless
https://github.com/topics/summarization
https://www.linkedin.com/pulse/train-your-large-language-model-llm-hugging-face-datasets-souvik-bose
https://www.kdnuggets.com/2023/04/text-summarization-development-python-tutorial-gpt35.htm
https://huggingface.co/docs/transformers/tasks/language_modeling
https://blog.devgenius.io/build-your-own-llm-model-using-openai-dd2be7fe9bb2
https://github.com/yaodongC/awesome-instruction-dataset
https://towardsdatascience.com/using-large-language-models-as-recommendation-systems-49e8aeeff29b
https://paperswithcode.com/paper/palr-personalization-aware-llms-for
https://github.com/Mohammadhia/t5_p5_recommendation_system
https://blog.reachsumit.com/posts/2023/05/tuning-llm-for-recsys/
https://www.catalyzex.com/s/Recommendation
https://ashukumar27.medium.com/the-agents-of-ai-1402548e9b8c
https://bytewax.io/blog/LLM-in-streaming
https://github.com/AI4Finance-Foundation/FinGPT