Knowledge-based Question Answering (KBQA) demands domain expertise, making large language models (LLMs) an attractive but costly solution. Researchers have been exploring how to combine the power of LLMs with smaller, more cost-effective knowledge graph models (KGMs). The challenge? Balancing accuracy and cost. A new approach called Coke tackles this head-on. It frames the problem as a multi-armed bandit, dynamically choosing between LLMs and KGMs based on the question's context. Coke uses a clever cluster-level Thompson Sampling method to estimate the accuracy of each model type, then refines its choice with a context-aware policy that considers the question's specific semantics. To keep costs in check, Coke incorporates a 'cost regret' constraint, penalizing models that burn through budget on incorrect answers. The results? Coke outperforms even GPT-4 on several benchmark datasets while significantly reducing costs—sometimes by over 20%. This research opens exciting possibilities for making powerful AI more accessible and affordable.
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Question & Answers
How does Coke's cluster-level Thompson Sampling work to balance LLM and KGM usage?
Coke's cluster-level Thompson Sampling is a probabilistic method that optimizes model selection between LLMs and KGMs. It works by first clustering similar questions and tracking the performance of each model type within these clusters. The system maintains probability distributions of model accuracy for each cluster, updates these based on actual performance, and uses this information to make informed decisions about which model to use for new questions. For example, if KGMs consistently perform well on simple factual queries about company data, the system would learn to prefer KGMs for similar questions, saving costs while maintaining accuracy. This adaptive approach enables Coke to achieve up to 20% cost reduction while maintaining high accuracy levels.
What are the benefits of combining AI language models with knowledge graphs?
Combining AI language models with knowledge graphs creates a more efficient and cost-effective system for handling questions and information processing. This hybrid approach offers the best of both worlds: the deep understanding and flexibility of language models, plus the structured, reliable information from knowledge graphs. Benefits include reduced operational costs, more accurate answers, and faster processing times. For businesses, this could mean better customer service chatbots that can answer both complex queries and simple factual questions without breaking the bank. This combination is particularly valuable in fields like healthcare, finance, and customer service where both accuracy and cost-efficiency are crucial.
How can businesses reduce their AI implementation costs while maintaining quality?
Businesses can reduce AI implementation costs while maintaining quality through several strategic approaches. First, implementing hybrid systems that combine different AI technologies, like using smaller specialized models alongside larger language models. Second, optimizing model selection based on task requirements - using simpler models for basic tasks and advanced models only when necessary. Third, incorporating cost-conscious algorithms that automatically balance performance with expenses. For example, a customer service system could use simple models for basic queries and only escalate to more expensive models for complex issues. This approach can lead to significant cost savings while maintaining service quality.
PromptLayer Features
Testing & Evaluation
The paper's multi-model evaluation strategy aligns with PromptLayer's A/B testing and performance comparison capabilities
Implementation Details
Configure A/B tests between LLM and KGM responses, track accuracy metrics and costs, implement Thompson Sampling logic for model selection
Key Benefits
• Automated performance comparison across model types
• Cost tracking per model and query type
• Data-driven model selection optimization
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