Using decoding methods in transformers with GPT-2: Greedy, Beam, and Sampling

AI Impact Summary

The article outlines practical decoding strategies for autoregressive generation using transformers and demonstrates them with the GPT-2 model via the Hugging Face transformers library in PyTorch. It highlights tradeoffs: greedy decoding is fast but tends to repetition; beam search improves fluency but can still produce repeats and higher compute, while n-gram penalties like no_repeat_ngram_size help curb repetition. For production teams, selecting decoding configuration will impact latency, cost, and output quality, so performance profiling with your target context and safety constraints is essential.