Can a Lightweight Multimodal Model Estimate LLM Reasoning Performance? A Study for Compute-Optimal Document Inference
Researchers have proposed a new model called BudgetDoc that can estimate the performance of large language models (LLMs) in document-related tasks. The model, which is multimodal and has about 1 billion parameters, predicts ordinal model performance across different budget levels. In experiments, it was shown to match or improve F1 scores compared to always-maximum-budget baselines while reducing cost. The study suggests that BudgetDoc could be used for dynamic allocation of
Researchers have proposed a new model called BudgetDoc that can estimate the performance of large language models (LLMs) in document-related tasks. The model, which is multimodal and has about 1 billion parameters, predicts ordinal model performance across different budget levels. In experiments, it was shown to match or improve F1 scores compared to always-maximum-budget baselines while reducing cost. The study suggests that BudgetDoc could be used for dynamic allocation of reasoning budgets in LLMs.
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Why it matters: This research matters because it addresses the issue of over-thinking penalties in document tasks, where visual layouts drive complexity. By providing a way to estimate model performance across different budget levels, researchers can optimize their models' performance while reducing cost.
Source: https://arxiv.org/abs/2608.18591
This article was originally published at: https://arxiv.org/abs/2608.18591