The choice of a pre-training objective function is a critical decision as it directly impacts the performance and effectiveness of the subsequent fine-tuning tasks. Several considerations should be taken into account when choosing the right pre-training objective function.

  1. Language Modeling: Language modeling, where the objective is to predict the next word given the previous words, is one of the most commonly used pre-training objectives. It is effective as it captures the syntax, semantics, and contextual information of the language. However, it may suffer from exposure bias and struggles with capturing long-range dependencies.

  2. Masked Language Modeling: Masked Language Modeling (MLM) has gained popularity with models like BERT. In MLM, random tokens in the input are masked, and the model is trained to predict the correct word. MLM helps in capturing bidirectional context and allows the model to learn contextual representations effectively.

  3. Translation: Another pre-training objective is translation, where the model is trained to translate text from one language to another. Translation pre-training helps in learning cross-lingual representations and improves multilingual tasks' performance.

  4. Alignment-based Objectives: Some models use alignment-based objectives like predicting word alignments or sentence-level relations. These objectives help in learning alignment between different parts of the sentence and understanding the relationships between sentences.

  5. Supervised Objectives: In some cases, pre-training can be performed using supervised objectives, where the model is trained on a large annotated dataset. For example, models can be trained on reading comprehension tasks, named entity recognition, or sentiment analysis. This approach can be particularly useful when fine-tuning for specific downstream tasks where labeled data is available.

  6. Task-Specific Objectives: Pre-training objectives can also be tailored to specific downstream tasks. For example, if the goal is to improve sentiment analysis, a polarity-detection objective can be used during pre-training.

  There is no one-size-fits-all objective function for pre-training as it depends on the specific requirements, data availability, and downstream tasks. It is recommended to evaluate different objective functions through experiments and choose the one that consistently leads to better performance on target tasks.