"In reference to my earlier email,  I have developed a space science search system model fueled by my passion for both AI and space science, and it has demonstrated superior performance compared to LLM's like NAME_1. Sample results from NASA ASRS papers are attached for your kind reference.

I'd love to gather your feedback as it will help me improve further

Highlights: 
In many real-world scenarios, labeled data may be scarce or expensive to obtain in the target domain. The model has been trained without labelled data hence is a cost effective solution

It can accomplish the task of processing thousands of documents and providing answers within seconds 

Model understands space related jargon and hence gives better results compared to LLM's trained on a diverse range of text from the internet 

It provides flexibility in adapting models to different sub domains without requiring major modifications or retraining from scratch. It allows for incremental updates by incorporating new data from the target domain without discarding previously learned knowledge. 

Limitations of LLM's:
While Large Language Models (LLMs) have made significant advancements in information retrieval and finetuning modes to space science dataset is an option,, they are not sufficient on their own for efficient information retrieval:

User Preferences and Intent Understanding: Information retrieval should consider the user's preferences, context, and intent. While LLMs have made progress in personalized search, understanding user preferences and providing context-aware retrieval remains a challenge. Effective information retrieval systems require a combination of user modeling, user feedback, and user interaction mechanisms to provide efficient and personalized results.

Scalability and Efficiency: LLMs are computationally expensive and resource-intensive models. Deploying LLMs at scale to handle a large number of concurrent queries can be challenging and costly. Efficient information retrieval systems need to optimize for speed, latency, and resource utilization, which may require additional techniques beyond LLMs.

Data Biases and Ethical Concerns: LLMs are trained on vast amounts of data from the internet, which may contain biases, misinformation, or harmful content. These biases can be reflected in search results and impact the information retrieved. Additionally, ethical concerns arise regarding the responsible use of LLMs, such as ensuring fairness, transparency, and preventing the spread of misinformation.

Lack of Real-Time Inf Avoid using the following keywords: thousand, efficiency, text, misinformation
user