# Contemporary Approaches in Evolving Language Models
## 3 Neural Network Models
### Pretrained Language Models
#### 3.2.1 Bert
Additionally, its bidirectional nature, which considers not only the preceding context in a sentence but also the subsequent one, enables the model to understand semantic relationships between phrases. Once pre-trained, BERT can be fine-tuned for specific tasks such as named-entity recognition, text classification, text generation, and more. Since the introduction of BERT, several improvements and variations have been made to further optimize its performance and applicability. Some of these enhancements are provided below (Table 1). FinBERT (Financial Sentiment Analysis with Pre-trained Language Models) is a variation of BERT specifically trained for processing financial data to perform sentiment analysis [39]. It is adept at understanding specialized \"investment jargon\" and precisely gauging the emotional tone of a text. It is trained using text from financial news services and data from the FiQA dataset. RoBERTa (A Robustly Optimized BERT Pretraining Approach) is an enhanced version of BERT developed by Facebook AI. While built on BERT's foundations, RoBERTa incorporates a more refined pretraining process, such as longer sequences, larger datasets, and dynamic training parameter tuning. It employs an advanced methodology, significantly more computational resources, and substantially more data. Additionally, RoBERTa introduces dynamic masking that changes during training [40]. ALBERT (A Lite BERT for Self-supervised Learning of Language Representations) is a model developed by Google Research which focuses on optimizing BERT's parameter usage. It utilizes a \"factorized embedding parameterization\" architecture to reduce the total number of network parameters, allowing for more scalable models with constrained resources [41]. XLNet (Generalized Autoregressive Pretraining for Language Understanding) models concepts from BERT and Transformer-XL. Unlike BERT, which predicts masked words, XLNet considers the complete sentence context, aiding the model in capturing long-term dependencies [42]. DeBERTa (Decoding-enhanced BERT) is built upon RoBERTa with added decoding enhancements, dynamic masking, and a more intricate attention mechanism. In tests, it exhibited superiority over human benchmarks in the SuperGLUE challenge [43]. DistilBERT (Distillation of BERT Model for Sequence-to-Sequence Tasks) is a streamlined version of BERT created using the knowledge distillation method. It learns to transfer the knowledge from a more complex model, like BERT, into a more compact form, making it faster and more efficient in operation [44]. Electra (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) is a model designed by Google Research. It introduces an efficient pretraining method in which random words are replaced with words predicted by the model. This enables a more effective utilization of supplementary resources. Despite this, DistilBERT \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Key Features** & **Features of Pretraining** & **Parameters** \\ \hline BERT & Based on the Transformer architecture. & Masking 15\% of words in a sentence. & 110 M (BERT-base) \\  & Uses masked language models (MLM) for pre-training. & Predicting the masked words. & \\ RoBERTa & Optimized BERT. & More data and batches during & 340 M (BERT-large) \\  & Removes NSP. & pre-training. Reduced use of masking. & 125 M (RoBERTa-base) \\  & Factorization of weight parameters. No NSP. & Uses MLM. & 125 M (RoBERTa-base) \\  & Uses Transformer-XL. & Uses permutation language models & 355 M (RoBERTa-large) \\  & Combines BERT and GPT. & (PLM). & 12 M (ALBERT-base) \\  & Introduction of enriched attention matrices. & MLM & 18 M (ALBERT-large) \\  & Dynamic weighting of different attention layers. & Uses Replaced Token Detection instead & 110 M (XLNet-base) \\ DistilBERT & Compressed version of BERT—40\% fewer parameters & MLM & 110 M (ALBERT-large) \\  & while maintaining 97\% performance. & Uses Replaced Token Detection instead & 110 M (XLNet-base) \\ \hline \hline \end{tabular}
\end{table}