1. Where are LLMs heading?
In this video, we will explore the future of LLMs and the exciting research and development happening in this field.
By the end, we will better understand the potential advancements and focus areas in the LLM space.
2. Journey so far
Before we jump into the future, let's quickly recap what we have learned in our journey with LLMs. We have covered the basics of LLMs,
3. Journey so far
their applications in NLP,
4. Journey so far
and the training processes involving data consideration, ethics, and environmental impact.
5. Journey so far
Now, it's time to look ahead and explore what lies on the horizon for LLMs.
6. Model explainability
Model explainability is a critical aspect of future research.
As LLMs become more powerful, it's crucial to understand how they arrive at their outputs.
Imagine an LLM planning our road trip. We would be interested to know answers to questions such as, "Why did the model choose this particular route?" and "Why did it suggest these specific spots for me to visit?".
Explainability builds trust in the technology and ensures we can identify and correct any biases or errors in the model's decisions.
7. Efficiency
In addition to explainability, developers are working on boosting LLMs' computational efficiency for quicker, less power-intensive outputs.
Research efforts in model compression and optimization are ongoing, speeding up data processing to save energy and time.
These improvements will result in better storage management and lower energy consumption, making LLMs more sustainable and cost-effective.
These steps can promote green AI, make LLMs viable on devices with limited resources, and reduce operating costs, improving accessibility and sustainability.
8. Unsupervised bias handling
Earlier, we learned that data bias is a key consideration in building LLMs, which can have dire consequences resulting in discrimination.
Handling bias in an unsupervised manner is an exciting area of research that explores methods and techniques to detect and mitigate biases automatically.
Unsupervised means that the LLM algorithm of the future would not need explicit human-labeled data; instead, it would autonomously identify and reduce biases by analyzing patterns within the training data.
As much as it sounds like a promising area of research, biases can be subtle and hard for algorithms to detect without human guidance, giving rise to the fear that new biases might get introduced in this process.
9. Enhanced creativity
LLMs have exhibited creativity in text-based art forms like poetry and storytelling, and in conjunction with other AI models, they have produced visual art and music.
They generate artistic content based on learned patterns from training data, not from emotional understanding or consciousness.
Despite creating human-like artistic content, they don't comprehend art or emotions like humans. Some advancements are exploring the ability for LLMs to demonstrate human-like emotional behavior, enhancing the interaction between humans and computers.
The future scope of LLMs in emotion inference is a subject of ongoing research and discussion.
10. Let's practice!
The landscape of LLMs is evolving rapidly with several exciting areas of research emerging.
Now, it's time for you to apply the knowledge you have gained.