How do you interpret and explain the outputs of transformer models?

In sentiment analysis tasks, transformer models classify a text as expressing a positive, negative, or neutral sentiment. The output of the model can be interpreted as a probability distribution over the possible sentiment labels. The label with the highest probability can be selected as the output, or a threshold can be used to determine the label based on the probabilities.

To interpret the output from a transformer model, several pieces of information should be taken into consideration. First, the model should be evaluated against the training data to ensure it is accurately representing the input and making accurate predictions. Second, the precision, recall, and accuracy of the model should be measured. By evaluating these metrics, the performance of the model can be gauged and any errors can be identified. Finally, the context in which the output is derived should be taken into account. For example, an output that is generated in an artificial intelligence context should be evaluated differently than an output generated in a natural language context.

It is impossible to explain inference of instances of Model Based Learners (from Perceptrons to Transformers). Weights of MBL nodes are laboriously balanced but bear no associations with input patterns they(nodes) are supposed to respond to. Weights are just weights. On the other hand, species of Instance Based Learners (https://en.wikipedia.org/wiki/Instance-based_learning) are natively explainable, though do not scale [at all]. Any inference is a voting act with voting experts' identities (underlying patterns) and properties known. Representation of a long text (sequence, vector) as a set of skip-grams helps solve scaling, generation and classification: https://www.dhirubhai.net/posts/activity-6873695912426917889-Rjri

The output of the transformer model can be evaluated using precision, recall, and accuracy metrics. Precision is a measure of how many positive predictions were made out of all predictions made. Recall is a measure of how many positive predictions were made out of all actual positive inputs. Lastly, accuracy is a measure of how the model’s predictions match the actual input data. By evaluating these three metrics, the performance of the model can be determined.

Identifying the sources of errors in the output of transformer models is crucial for accurate interpretation and explanation. Errors can originate from various factors, including data quality, model design, training process, and inference settings. For instance, noisy or biased data, insufficient or overfitting during training, inappropriate hyperparameters, or incorrect decoding strategies can all contribute to errors in the output. It's essential to thoroughly investigate these potential sources of errors to gain a better understanding of how they impact the model's output.