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Sunday, 7 July 2024

How to Interview and Hire ML/AI Engineers

Eugene Yan 2 years ago 47

The article discusses frameworks for interviewing and hiring machine learning and AI engineers, covering technical skills like software engineering proficiency, data literacy, understanding ML model opacity, and evaluation methodologies. Key technical assessment areas include coding exercises (30-60 minutes), data analysis skills, comfort with model uncertainty, and knowledge of evaluation practices, with specific follow-up questions provided for each. For more research-focused roles, additional assessment includes science breadth (familiarity with ML domains like recommender systems and language modeling), science depth (rigor on past projects), and science application (solving practical problems relevant to the team), alongside non-technical dimensions like ambiguity tolerance, influence, complexity, and execution.

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Extrinsic Hallucinations in LLMs

Lil'Log 2 years ago 37

Researchers distinguish between in-context hallucination (output inconsistent with provided context) and extrinsic hallucination (output not grounded in the model's pre-training data or world knowledge). Extrinsic hallucination occurs when large language models fabricate information rather than admitting knowledge gaps. To reduce extrinsic hallucination, LLMs must generate factual outputs while explicitly acknowledging when they lack information about a topic.

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Large Language Models

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