Neural language models can be seen parallel to COTS software, developed and utilized widely in the AI domain. They apply artificial neural networks to predict the next item in a sequence, particularly in the context of words. Based on the previous input data, these models can anticipate the most likely subsequent word or phrase, thereby facilitating natural language understanding and generation.

• Robustness: Neural language models are potent tools capable of comprehending and generating human-like text, owing to their capacity to process vast amounts of data.
• Sophisticated Abilities: Neural language models learn to predict the likelihood of a sequence of words or phrases appearing in a sentence by training on a large corpus of text. They take into account the context, insert suitable words or phrases and generate human-like text that is highly coherent and sophisticated.
• General Use: Deployed in a wide array of applications ranging from simple tasks like word prediction to more complex ones such as speech recognition, virtual customer support, and natural language processing.
• Adaptability: They have an inherent capability to adapt their functionality with new data, learning, and improving the prediction accuracy.

Implementing a neural language model involves thoughtful analysis coupled with strategic planning. It commences with identifying the specific use case and gathering relevant data. Following that, the NLM is trained, validated, and deployed while continually monitoring its performance and making the necessary adjustments. Organizations must address both technical considerations like system architecture, training time, computational resources, and ethical considerations like bias and transparency in implementing NLMs.

Many organizations and developers find the application of Neural Language Models advantageous due to several reasons:

• Advanced Predictions: Neural language models, trained on vast amounts of data, can make complex predictions that capture linguistic patterns, contextual nuances, and long-term dependencies between words. This makes them significantly superior to traditional language models.
• Flexibility: Given that NLMs are adaptable, they can learn from new data and improve. This feature proves beneficial for organizations aiming for efficiency and optimized predictions.
• Lower Cost: Neural language models provide a cost-effective way to handle natural language processing tasks as they do not require extensive manual interventions or rule-based programming.
• Broad Applications: The versatile nature of NLMs makes them applicable across diverse areas, encompassing language translation, chatbots, automated proofreading, content creation, etc.
• Availability of Pre-trained Models: Leverage of pre-trained models like GPT-3, BERT, etc., can substantially circumvent the need for large datasets, dedicated hardware, or extensive computational resources.

Despite their promise and capabilities, neural language models also come with certain limitations:

• Data Hungry: NLMs require an enormous amount of data for training, which could prove to be a limitation when dealing with less common languages or specific domains where data is scarce.
• Opacity: These models often act as black boxes, making their workings and decisions hard to interpret or explain, thereby raising issues surrounding transparency and accountability.
• Resource Intensive: Training neural language models is computationally intensive, requiring substantial hardware resources and time.
• Bias: As NLMs learn from existing data, they can inadvertently learn and perpetuate existing biases in the data. This issue of bias calls for thoughtful handling and sometimes additional preprocessing.

In conclusion, neural language models, while powerful, come with their own set of challenges and limitations. Successful deployment requires cognizance of these challenges and leveraging the underlying strength of these models for progress and innovation in natural language processing and related fields.