What are the best practices for using transformers to generate text in AI?

2 Use appropriate input formats and prompts

Transformers can generate text from various types of inputs, such as plain text, keywords, images, or speech. However, not all inputs are equally effective for generating high-quality text. You need to consider the format, length, and content of your inputs, and how they relate to your output. For example, if you want to generate a summary of a long article, you may want to use a short and concise input that captures the main points, rather than the entire article. You may also want to use prompts that guide the model to generate the desired output, such as adding a prefix or a suffix to indicate the task or the genre.

3 Control the output length and diversity

One of the challenges of text generation with transformers is to control the length and diversity of the output. You don't want the output to be too short or too long, or to repeat the same words or phrases over and over. To control the output length, you can use parameters such as maximum length, minimum length, or early stopping, which determine when the model stops generating tokens. To control the output diversity, you can use parameters such as temperature, top-k, or top-p, which affect the probability distribution of the next token. You may need to experiment with different values of these parameters to find the optimal balance between coherence and creativity.

4 Evaluate the output quality and relevance

Another challenge of text generation with transformers is to evaluate the quality and relevance of the output. You may want to measure how well the output matches your expectations, goals, and standards. There are different ways to evaluate text generation, such as automatic metrics, human ratings, or task-based metrics. Automatic metrics, such as BLEU, ROUGE, or METEOR, compare the output with a reference text and compute a numerical score based on similarity. Human ratings, such as fluency, grammaticality, or informativeness, ask human evaluators to rate the output on various criteria. Task-based metrics, such as accuracy, recall, or precision, measure how well the output performs on a specific task, such as answering a question or summarizing a document.

5 Address the ethical and social implications

Text generation with transformers is not only a technical problem, but also an ethical and social one. You need to be aware of the potential risks and harms that may arise from generating text with transformers, such as bias, misinformation, plagiarism, or abuse. You need to ensure that the data you use to train and fine-tune the model is diverse, representative, and respectful of the people and topics involved. You need to monitor and moderate the output you generate and distribute, and provide clear and transparent information about the source, purpose, and limitations of the text. You need to respect the rights and interests of the stakeholders, such as the authors, the users, and the society.