Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees
arXiv:2410.15761v4 Announce Type: replace-cross Abstract: Large Language Models excel in generative tasks but exhibit inefficiencies in structured text selection, particularly in extractive question answering. This challenge is magnified in resource-constrained environments, where deploying multiple specialized models for different tasks is impractical. We propose a Learning-to-Defer framework that allocates queries to specialized experts, ensuring high-confidence predictions while optimizing computational efficiency. Our approach integrates a principled allocation strategy with theoretical gu
The increasing scale and cost of large language models are pushing researchers to find more efficient and specialized deployment strategies, especially in resource-constrained environments.
This research provides a framework for optimizing LLM deployment, which can significantly reduce operational costs and improve performance for specific tasks like extractive QA, making advanced AI more accessible.
The ability to intelligently allocate queries to specialized AI models, rather than relying on a single, inefficient general-purpose LLM, changes the paradigm for deploying sophisticated AI systems.
- · Companies with constrained compute resources
- · Developers of specialized AI models
- · Cloud computing providers offering fine-tuned models
- · Businesses using extractive QA for large datasets
- · Developers of generalized, inefficient LLMs
- · Organizations with undifferentiated compute strategies
Enterprises will begin to adopt more modular and cost-effective AI architectures for specific tasks.
This efficiency could accelerate the adoption of LLMs in new sectors where resource constraints were previously prohibitive.
Increased adoption of specialized AI agents could further fragment the AI market, leading to a proliferation of niche AI applications.
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Read at arXiv cs.LG