I used QAG to implement an LLM text summarization evals

Dec 19, 2023 ·

This article provides a step-by-step guide to evaluating an LLM text summarization task. The first step is to define the task and its objectives. This includes setting the task parameters such as the number of words, the size of the dataset, and what type of summarizer (e.g., abstractive or extractive) will be used.

Next, the data should be prepared. That means preprocessing the text by tokenizing, cleaning, and splitting into train and test sets. Preprocessing also includes selecting an appropriate evaluation metric to measure summarization performance.

The third step is to choose an appropriate language model for summarization. Selecting a language model involves considering the size of the model, its computational complexity, and the amount of training data needed. Once the model is chosen, it can be trained using a variety of techniques.

Once the model has been trained, it needs to be evaluated. Evaluation involves comparing the output of the model to a set of human-created summary summaries. It is important to consider the quality of the summaries generated by both the model and humans in order to assess how accurately the model is able to summarize the input text.

Finally, the results should be analyzed. This helps to determine whether the model is providing meaningful summaries and if any improvements could be made. For example, if the model is not performing to expectations, adjustments may need to be made to the data preprocessing, model architecture, or training techniques.

Overall, this article provides a comprehensive overview of the steps involved in evaluating an LLM text summarization task. Through proper data preparation, model selection, training, and evaluation, developers can ensure that their summarization system is providing accurate and useful summaries.