Disainige relevantsuse hindamise süsteem otsingutulemustele koos LLM-judge'iga.

For designing a relevance evaluation system for search results using LLM-judge, the following approach can be proposed:

1. Data collection: Obtain search queries and corresponding results.

2. Preprocessing: Normalize the text of queries and results, removing noise.

3. Integration of LLM-judge: Use a large language model (LLM) as a "judge" to assess relevance. For each result, LLM receives the query and the text of the result and provides a relevance score on a specified scale.

4. Calibration and training: If possible, collect labels from users or experts to train or calibrate the model, improving the accuracy of assessments.

5. Aggregation of scores: For each query, aggregate the LLM scores for the results to rank them.

6. Monitoring and feedback: Implement a system to monitor the quality of assessments and collect feedback for continuous improvement.

This approach allows leveraging the power of LLM for a more nuanced and contextual relevance assessment, which improves the quality of search results.