A Developer's Guide to Evaluating LLMs Beyond Demo Results

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An article on DEV Community argues that impressive LLM demonstrations can hide a model's true strengths and weaknesses, so developers should look further. It outlines key evaluation dimensions: performance metrics such as BLEU and ROUGE, task-specific testing across use cases, and model complexity and training details. The author illustrates the process with a hypothetical Qwen-style model tested on factual questions and on tasks like news summarization and translation. The piece also flags common pitfalls, including assuming demo success transfers to real-world settings, relying only on generation quality, and ignoring training choices.

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ProgrammingDEV Community ·

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