Adaptation of Pre-trained Models via Full Fine-Tuning

Freezing Encoder Parameters During Fine-Tuning

Evaluating the Direct Application of a General Language Model

A team develops a large language model by training it on a vast collection of text from the internet, with the sole objective of making it proficient at predicting the next word in a sequence. They then attempt to use this model directly, without any changes, to categorize customer support emails into 'Billing Issue', 'Technical Problem', or 'Feature Request'. The model performs poorly. Which of the following statements best explains this outcome?

Mismatch Between Pre-training and Downstream Objectives

Encoder-Classifier Model Notation

Parameterized Prediction Function using a BERT model

Classification via an Encoder Function ($Classify_{\omega}(Encode_{\hat{\theta}}(\cdot))$)

Consider two functions, $M_{\alpha, \beta}(x)$ and $M_{\gamma, \delta}(x)$. Both functions are designed to perform the same underlying computational task. However, when given the exact same input value for $x$, they produce different results. Based on the provided notation, what is the most likely reason for this difference in output?

A machine learning model, designed to perform a specific task, is represented by the function $M$. Initially, its performance is poor. After a training process that adjusts the model's internal settings, its performance on the same task improves significantly. Let the set of internal settings before training be denoted by $\theta_{initial}$ and after training by $\theta_{final}$. Which notation correctly represents the model before and after training, respectively, when applied to an input $x$?

Explaining Model Behavior Change

Adaptation of Pre-trained Models via Full Fine-Tuning