LLMs are characterized by:
- Scale: They contain millions, billions, or even hundreds of billions of parameters
- General capabilities: They can perform multiple tasks without task-specific training
- In-context learning: They can learn from examples provided in the prompt
- Emergent abilities: As these models grow in size, they demonstrate capabilities that weren’t explicitly programmed or anticipated
The advent of LLMs has shifted the paradigm from building specialized models for specific NLP tasks to using a single, large model that can be prompted or fine-tuned to address a wide range of language tasks. This has made sophisticated language processing more accessible while also introducing new challenges in areas like efficiency, ethics, and deployment.
However, LLMs also have important limitations:
- Hallucinations: They can generate incorrect information confidently
- Lack of true understanding: They lack true understanding of the world and operate purely on statistical patterns
- Bias: They may reproduce biases present in their training data or inputs.
- Context windows: They have limited context windows (though this is improving)
- Computational resources: They require significant computational resources
Why is language processing challenging?
Computers don’t process information in the same way as humans. For example, when we read the sentence “I am hungry,” we can easily understand its meaning. Similarly, given two sentences such as “I am hungry” and “I am sad,” we’re able to easily determine how similar they are. For machine learning (ML) models, such tasks are more difficult. The text needs to be processed in a way that enables the model to learn from it.
Even with the advances in LLMs, many fundamental challenges remain. These include understanding ambiguity, cultural context, sarcasm, and humor. LLMs address these challenges through massive training on diverse datasets, but still often fall short of human-level understanding in many complex scenarios.
