Where Do LLMs Struggle the Most?🤔
Where Do LLMs Struggle the Most?🤔
Think of an LLM as a student on an academic journey. It begins with pre-training, where models like GPT-4, PaLM and LLaMA absorb knowledge from massive amounts of text and multimodal data. Once they have this foundation, they move to fine-tuning. Here they specialize through instruction tuning, alignment techniques such as RLHF(Reinforcement Learning through Human Feedback) and efficient methods like LoRA.
To make these models practical, researchers work on efficiency. Techniques like quantization and pruning shrink the computational load so they can run on real hardware. After that comes evaluation, where models are tested on summarization, translation, reasoning, classification and sentiment analysis.
Challenges such as bias, memorization, safety, toxicity and high costs continue to demand attention.
Despite these issues, LLMs are already reshaping medicine, education, law, finance, science, robotics and coding.
Read the full research: https://hubs.li/Q03J6CP_0
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