Not an LLM.
Not much learning either.
LLMS
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Beyond Auto-Regressive: Future AI Architecture Requirements
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Ever.
Whatever system will be able to do this will not be an Auto-Regressive LLM. -
LLM Size vs Genome: Knowledge Encoded vs Learned
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A small LLM is 14GB.
Your entire genome is 800MB.
Most of it encodes basic biological functions.
A small part encodes brain circuitry.
The difference between your genome and a chimp's genome is 8MB.
Not enough to encode the knowledge you mention.
Much of that must be learned. -
Evaluating LLMs: Key Metrics for Benchmarking Performance
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To evaluate and benchmark LLMs effectively, familiarize yourself with relevant metrics aligning with your goals. Understanding your LLM's performance is crucial for success.
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Master LLMs: Top Strategies to Evaluate
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Find more details in the newsletter iteration: https://
louisbouchard.substack.com/p/master-llms-
top-strategies-to-evaluate
… Subscribe to the newsletter for more weekly AI insights like these! -
Key Benchmarks for Evaluating LLM Reasoning and Coding Abilities
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Use Hellaswag and ARC metrics for reasoning tasks, MMLU and Truthful QA for truthful LLMs, and HumanEval for coding-oriented LLMs. They'll test your model's ability and reveal overlooked weaknesses.
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Perplexity Metric Limitations in AI Model Evaluation
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BUT, Relying solely on Perplexity metric can lead to models memorizing vs. understanding data. It also fails in complex tasks like summarizing or script writing.
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Evaluating Custom LLMs vs Closed-Source Models Performance
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Closed-source models like GPT-4 or Claude yield good performance due to the powerful teams and resources behind them. But for specialized apps, consider shifting to your own LLM and comparing results. Evaluate an LLM's effectivity through the Perplexity Evaluation Metric.
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Choosing the Right LLM for Your Specific Use Case
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Exploring Large Language Models (#LLMs) like closed-source GPT-4, Claude, open-source Llama, Falcon, or your own fine-tuned model can be daunting. Choosing correctly, considering your specific use case is crucial…