## 3 HELIOS Concept in the Real Estate Market--Methodology and Implementation
### Helios System Interaction Description  
The main component of the HELIOS system is the LLM. Working with a large language model (LLM) in the context of real estate data analysis constitutes a continuous interactive process that evolves with each subsequent query and response. Each interaction can generate new knowledge. The evaluation of this knowledge by system components leads to iterative validation and model improvement through backpropagation techniques. A key aspect is the tokenization of textual data, which transforms it into 768-dimensional vectors in the BERT LLM model space, essential for semantic analysis. The chronological presentation of the work cycle in real estate data processing is presented in Figure 3.  
The detailed chronological description of the work cycle, which includes all key elements of the system and interrelationships, contains the following elements. --Building or Acquiring the LLM: An existing language model, BERT (bert-base-uncased), was used, which is a base model with 12 layers, prepared to work with text in lowercase form. The choice of this model reduces the time needed for fine-tuning by leveraging transfer learning. --Preparing Input Data: Real estate transactions from the Gdansk market were collected and filtered for completeness and correctness of address definitions. Additionally, a substantial number of scientific articles and documents in the real estate market were used, enriching the model with specialized knowledge. --Substantive Selection and Data Evaluation: All collected data were substantively evaluated to ensure they are appropriate and comprehensive for the system's needs. --ETL (Extract, Transform, Load) Process: In the transformation phase, numerical data were recorded in words with references, facilitating a better understanding of the language model. Locational data were converted from verbal records to numerical val  
Figure 3: HELIOS sequence diagram. Source: own study. ues in the WGS84 system, then standardized and recorded in words again to facilitate semantic analysis. --Domain Adaptation: Data processed in the ETL phase were used for domain-specific pre-training of the BERT model, allowing better adaptation to the specifics of the real estate market. --Real Estate Data Landscape Reality: The system manages diverse and unstructured data, collecting and analyzing information from multiple sources. --Artificial Homogeneous Property Database with Flaws: A specialized database containing intentionally introduced errors was created to test the model's ability to identify and handle potential anomalies and errors. --General LLM: The system's primary component that interprets and understands linguistic patterns, which is essential in the data analysis process. --Early Validation: Early validation of results by real estate market experts allows for iterative improvement of model accuracy. --HELIOS Nexus: Enhances cognitive processes by using custom LLM resources to increase analysis efficiency. --Released LLM: A specially adapted language model used for analyzing and interpreting real estate market data. --Using the LLM for Semantic Characterization of Real Estate Data: Data post-ETL process is tokenized, and each transaction is placed in the BERT model's semantic multidimensional space, where each gets its unique \"fingerprint.\"  
--Real Estate Fingerprint: LLM technology is used to create detailed property profiles based on analyzing their features and characteristics. --Fingerprints Matching: The round-robin method is used to assess differences between pairs of objects through iterative analysis, allowing for accurate comparison of real estate profiles. --Non-Homogeneity Feedback: In unsatisfactory results, the system analyzes feedback to adjust and improve the model. --HELIOS Homogeneity Output: The final classification of homogeneous objects, confirming the effectiveness of the analysis and real estate matching.