大型語言模型在工具內學習的可證明優勢

摘要

配備了檢索、記憶或外部API的工具增強型語言模型正在重塑人工智慧領域，然而其理論優勢仍未被充分探討。本文通過展示在工具內學習（外部檢索）相較於在權重內學習（記憶）對於事實回憶的益處，來回應這一問題。我們證明，模型僅憑其權重所能記憶的事實數量，從根本上受其參數總數的限制。相反地，我們證明了工具使用能夠通過一個簡單且高效的電路構造實現無限制的事實回憶。這些結果在控制實驗中得到了驗證，其中使用工具的模型始終優於依賴記憶的模型。我們進一步表明，對於預訓練的大型語言模型而言，教授工具使用和通用規則比將事實微調進記憶更為有效。我們的工作提供了理論與實證的雙重基礎，確立了為何工具增強的工作流程不僅實用，而且可證明更具擴展性。

English

Tool-augmented language models, equipped with retrieval, memory, or external APIs, are reshaping AI, yet their theoretical advantages remain underexplored. In this paper, we address this question by demonstrating the benefits of in-tool learning (external retrieval) over in-weight learning (memorization) for factual recall. We show that the number of facts a model can memorize solely in its weights is fundamentally limited by its parameter count. In contrast, we prove that tool-use enables unbounded factual recall via a simple and efficient circuit construction. These results are validated in controlled experiments, where tool-using models consistently outperform memorizing ones. We further show that for pretrained large language models, teaching tool-use and general rules is more effective than finetuning facts into memory. Our work provides both a theoretical and empirical foundation, establishing why tool-augmented workflows are not just practical, but provably more scalable.

大型語言模型在工具內學習的可證明優勢

Provable Benefits of In-Tool Learning for Large Language Models

摘要

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