Newest papers

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

Korean

천연 언어 명령을 해석하는 것은 남아 있지만 강력한 데이터 명령은 종종 높은 수준의 추상에 의존한다, 예를 들어 에이전트가 "피라미드에서 블록을 쌓아"라고 요청하는 것과 같은 높은 수준의 추상에 의존합니다. 추상 지침을 해석하는 것은 모델이 스피커가 의도한 동일한 추상을 확인하고 그것을 인스턴트하는 많은 구체적인 행동으로 롤링하는 것을 요구합니다. 언어 모델은 여기서 싸우고 있습니다. 이전 작업은 명령의 부분에서 추상의 수준이 상승함에 따라 성능이 악화되는 것을 보여주었습니다. 자연 언어 추상과 구체적인 행동 사이의 격차를 제거하기 위해, 우리는 코딩 환경에서 {\em 도메인-특별 언어}의 개

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.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange