Computational Economics in Large Language Models: Exploring Model Behavior and Incentive Design under Resource Constraints
This work addresses computational inefficiency in LLMs, offering a principled method for designing more efficient and adaptive models, though it is incremental as it builds on existing training paradigms.
The paper tackles the problem of high computational costs in large language models (LLMs) by introducing a computational economics framework that treats LLMs as resource-constrained economies, resulting in models that achieve roughly a 40% reduction in FLOPS and lower latency while preserving accuracy on benchmarks like GLUE and WikiText-103.
Large language models (LLMs) are limited by substantial computational cost. We introduce a "computational economics" framework that treats an LLM as an internal economy of resource-constrained agents (attention heads and neuron blocks) that must allocate scarce computation to maximize task utility. First, we show empirically that when computation is scarce, standard LLMs reallocate attention toward high-value tokens while preserving accuracy. Building on this observation, we propose an incentive-driven training paradigm that augments the task loss with a differentiable computation cost term, encouraging sparse and efficient activations. On GLUE (MNLI, STS-B, CoLA) and WikiText-103, the method yields a family of models that trace a Pareto frontier and consistently dominate post-hoc pruning; for a similar accuracy we obtain roughly a forty percent reduction in FLOPS and lower latency, together with more interpretable attention patterns. These results indicate that economic principles offer a principled route to designing efficient, adaptive, and more transparent LLMs under strict resource constraints.