Inference Graph
A framework that abstracts multi-model collaboration as a graph structure, where nodes represent individual LLMs and edges represent conditional model activations. Threshold-based strategies applied to this structure enable optimal trade-offs between inference cost and performance.
Timeline (last 90 days)
A paper published on arXiv proposes a graph-structured framework called "Inference Networks" for composing multiple expert models.
In inference networks, nodes represent different LLMs and edges represent conditional model activation (i.e., triggering the next model call only when a certain condition is met).
The goal of the inference network design problem is to determine the optimal topology that best balances cost and performance.
The paper formalizes the problem as: minimizing expected inference cost subject to meeting a target performance constraint.
The paper provides structured threshold computation methods and confidence estimation mechanisms for discriminative and generative tasks respectively.
Experiments on open-source LLMs reported in the paper show that adopting the optimal threshold strategy can achieve substantial cost reductions while meeting pre-specified performance budgets.
The current theoretical results in the paper are primarily for the basic topology of "cascaded experts," and the optimal strategies for more complex inference network topologies (such as hybrid structures with branching and parallel ensembles) remain open problems.
The current framework assumes that the cost of each model is fixed and known, whereas cloud API pricing is often more complex.
All Facts (8)
The current framework assumes that the cost of each model is fixed and known, whereas cloud API pricing is often more complex.
50%UnverifiedIn inference networks, nodes represent different LLMs and edges represent conditional model activation (i.e., triggering the next model call only when a certain condition is met).
50%UnverifiedThe goal of the inference network design problem is to determine the optimal topology that best balances cost and performance.
50%UnverifiedThe paper formalizes the problem as: minimizing expected inference cost subject to meeting a target performance constraint.
50%UnverifiedThe paper provides structured threshold computation methods and confidence estimation mechanisms for discriminative and generative tasks respectively.
50%UnverifiedExperiments on open-source LLMs reported in the paper show that adopting the optimal threshold strategy can achieve substantial cost reductions while meeting pre-specified performance budgets.
50%UnverifiedThe current theoretical results in the paper are primarily for the basic topology of "cascaded experts," and the optimal strategies for more complex inference network topologies (such as hybrid structures with branching and parallel ensembles) remain open problems.
50%UnverifiedA paper published on arXiv proposes a graph-structured framework called "Inference Networks" for composing multiple expert models.
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