Explain LLM training and evaluation

Q: Explain LLM training and evaluation

This question evaluates an engineer's competency in LLM engineering, including transformer pretraining and finetuning, alignment approaches like instruction tuning and RLHF, strategies for reducing hallucinations, evaluation and production monitoring practices, and inference optimizations.

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Question

LLM Engineering: Training, Alignment, Hallucination Reduction, Evaluation, Monitoring, and Inference Optimization

Context

You are designing, aligning, evaluating, deploying, and optimizing transformer-based large language models (LLMs) for real-world applications.

Tasks

Explain how transformer-based LLMs are pretrained and finetuned.
Compare instruction tuning and RLHF (Reinforcement Learning from Human Feedback).
Discuss strategies to reduce hallucinations.
Describe how you would evaluate an LLM (e.g., perplexity, task accuracy, human evaluation) and monitor it in production.
List inference optimizations you would apply (e.g., quantization, KV caching).

Explain LLM training and evaluation

LLM Engineering: Training, Alignment, Hallucination Reduction, Evaluation, Monitoring, and Inference Optimization

Context

Tasks

Solution

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Explain LLM training and evaluation

Overview

LLM Engineering: Training, Alignment, Hallucination Reduction, Evaluation, Monitoring, and Inference Optimization

Context

Tasks

Solution

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