Using large language models to identify duplicate support tickets can become prohibitively expensive due to the quadratic growth in comparisons required. A naive approach of comparing every ticket with every other ticket results in a cost that scales with the square of the number of tickets. A more efficient method involves first categorizing tickets with an LLM and then comparing only tickets within the same category, which can reduce costs by up to 20 times. However, uneven distribution of tickets across categories can lessen these savings, suggesting further subdivision of large categories may be necessary. AI
IMPACT Highlights the significant cost implications of using LLMs for pairwise comparisons, driving the need for efficient categorization strategies in AI-powered support systems.
RANK_REASON Article discusses the cost-effectiveness and scaling challenges of using LLMs for a specific application (ticket deduplication), rather than a new model release or core research.
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