# Contemporary Approaches in Evolving Language Models
## 3 Neural Network Models
### Pretrained Language Models
#### 3.2.5 Bard  
Bard is one of the modern deep learning models released by Google in March 2023. Notably, Bard addresses the issue of reducing computational power requirements by creating an efficient AI model. Through reducing the number of parameters during training, the model becomes faster, cost-effective, and energy efficient. Its architecture also boasts greater scalability, enabling easy adaptation to a myriad of NLP tasks and applications [77].  
Based on the Transformer-XL architecture, Bard represents a large language model (LLM) designed for processing extensive text sequences. This architecture, an extension of the original transformer, excels in language modeling tasks by adapting to extended text sequences.  
Comprising two main components, an encoder and a decoder, Bard operates through a stack of internal attention layers. Self-attention facilitates the model's capacity to capture long-range word relations, an essential trait for generating coherent natural-language text. Bard also integrates a positional encoding layer, assisting the model in understanding the positions of words within the input text. Additionally, there is a residual connection layer, which prevents overfitting while maintaining model generalization, and a normalization layer, stabilizing the training dynamics for sequential learning. The model was trained using a comprehensive dataset encompassing various textual forms, such as books, articles, and code. Furthermore, by employing an MLM objective, the model predicts occluded words in a given text sequence.  
Unlike the ChatGPT chatbot, Bard, when generating responses, leverages current online information by accessing the Internet, offering more accurate and specific answers to queries. Other distinctions from ChatGPT are Bard's multilingual capabilities, which enable it to understand natural language in various linguistic contexts, catering to a broader audience. Furthermore, Bard was implemented based on the LaMDA language model (Language Model for Dialogue Applications) [78]. Following the advent of the Bard chatbot, Google opted to integrate it into its search engine, enhancing query processing to yield superior results. This enhancement arises from the model's ability to analyze the context and semantics of a query, which refines the precision of the results. This proves particularly beneficial for processing niche queries that traditional search engines might struggle to satisfactorily address.  
Experimental outcomes have demonstrated that the Bard model surpasses the GPT model in recognizing user intents, as well as in sentiment analysis and text classification.  
However, the Bard model does have its shortcomings. Despite its capacity to generate seemingly coherent text, a more profound understanding of factual accuracy, originality in expression, and the ability to capture the intentions and objectives of the text are essential for its optimal operation. Furthermore, the model's efficacy largely hinges on the quality of training data, necessitating meticulous curation and a timely examination of the influence of various datasets on language modeling.