アフォーダンスを活用したLLMによる部分的世界モデリング
Affordances Enable Partial World Modeling with LLMs
大規模言語モデルを部分的世界モデルとして捉え、アフォーダンスに基づく予測を抽出することで探索効率とタスク性能を向上させる手法を提案した論文。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Khimya Khetarpal, Gheorghe Comanici, Jonathan Richens, Jeremy Shar, Fei Xia, Laurent Orseau, Aleksandra Faust, Doina Precup
分類: cs.LG, cs.AI
原文アブストラクト
Full models of the world require complex knowledge of immense detail. While pre-trained large models have been hypothesized to contain similar knowledge due to extensive pre-training on vast amounts of internet scale data, using them directly in a search procedure is inefficient and inaccurate. Conversely, partial models focus on making high quality predictions for a subset of state and actions: those linked through affordances that achieve user intents~\citep{khetarpal2020can}. Can we posit large models as partial world models? We provide a formal answer to this question, proving that agents achieving task-agnostic, language-conditioned intents necessarily possess predictive partial-world models informed by affordances. In the multi-task setting, we introduce distribution-robust affordances and show that partial models can be extracted to significantly improve search efficiency. Empirical evaluations in tabletop robotics tasks demonstrate that our affordance-aware partial models reduce the search branching factor and achieve higher rewards compared to full world models.
