Summary:
The blog post “Inside GPT — II: The Core Mechanics of Prompt Engineering” explains the mechanics of prompt engineering in language models like GPT-2. It discusses the impact of prompt choice on text generation, explores decoding strategies like greedy search and beam search, and mentions the use of n-gram penalty to improve the coherence of generated text. The article also provides references and relevant links for further exploration.
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Inside GPT — II: The Core Mechanics of Prompt Engineering
The Compression of Human Text
Large language models compress the world through human text. In this post, we explore how to decode this compression and influence its output.
Model Training and Text Generation
During training, the model learns from human text projection and forms a probability distribution for text generation. The text data seen during training forms this probability.
Decoding the Probability Distribution
We can prompt the model with a sentence and extract the token with the highest probability to generate the next token. This approach, known as greedy search, simplifies text generation but may lead to repetitive sequences.
Introducing Beam-Search Decoding
Beam search considers future steps and calculates the joint probability, providing a more extensive approach to decode text with reduced repetition. However, it may still suffer from repetitive output.
N-Gram Penalty
By penalizing repeated pairs of word sequences, known as n-gram penalty, we can achieve more coherent and compact output.
Choosing Decoding Strategies
When factualness is paramount, like in key information extraction, greedy-search is preferred. For creative outputs, beam-search is often more suitable.
The Impact of the Prompt
The text generation is conditional on the prompt, influencing the probabilities formed for each word. Choosing the right prompt is crucial for desired text generation outcomes.
Alternative Methods and Further Exploration
Other methods, such as truncating the vocabulary distribution or sampling, can also influence the generated text. For a deeper exploration, refer to the article from Patrick von Platen in HuggingFace blog.
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