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From Abstraction to Action: DSLs for Natural Language Abstraction Interpretation and Execution

Amit Lisha, Reut Tsarfaty · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28311 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

解释抽象指令需要模型既识别发言人意图的抽象,又将其解读成许多实例化的具体行动。Laytex语言模型在这里挣扎:先前的研究表明,在指令中抽象指令的水平上升时,性能会下降。为了弥合自然语言抽象和具体行动之间的差距,我们建议从编码环境中借用一个 {\em domain-specific language}(DSL)的概念,这是一个明确的表示层,其记录的原始名称会随着抽象部分的反复下降而下降。为了证明DSLs在自然语言抽象和具体行动之间的差距,我们建议从编码环境中借用 {\em domain-specific language}(DSL)的概念,这是一个对抽象的明确的表现层的概念。在DSL1中,我们只用

Machine translation (Meta M2M100); the English original follows.

Abstract

Natural-language instructions often rely on high-level abstractions, such as asking an agent to ``stack blocks in a pyramid.'' Interpreting abstract instructions requires models to both identify the abstraction intended by the speaker, and unroll it into the many concrete actions that instantiate it. Language models struggle here: prior work has shown that performance degrades as the level of abstraction in the instruction rises. To bridge the gap between natural language abstraction and concrete actions, we propose to borrow from coding environments the notion of a {\em domain-specific language} (DSL), an explicit representation layer whose documented primitives name recurring abstractions that expand into concrete actions. We demonstrate the efficacy of DSLs for NL interpretation on \textsc{Hexagons}, a visually-grounded instruction-following task graded for abstraction. We further ask whether language models can induce such DSLs themselves from domain data. Our results show that while code generation consistently improves over direct inference, code generation over a human-curated DSL yields dramatic gains, up to 10 absolute F1 points, reaching the human skyline, with gains concentrated at {\em higher} abstraction levels. We confirm this pattern on the \textsc{Text2CAD} benchmark. At the same time, model-induced DSLs recover only part of this gain, remaining inconsistent across runs and short of their human-curated counterpart. Overall, the right representation closes much of the gap caused by high abstraction --- but inducing that layer of representation automatically is where models still fall short, and where we see a strong opportunity for the community to make progress.

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Topics: Abstraction, Computer science, Programming language, Interpretation (philosophy), Representation (politics), Abstract interpretation

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