arXiv AI By Angelos Poulis, Mark Crovella, Evimaria Terzi

Testing the Limits of Truth Directions in LLMs

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arXiv Computation and Language
Oct 21

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

The paper introduces domain-specific languages (DSLs) as an explicit representation layer to bridge the gap between high-level natural-language abstractions and concrete actions. Experiments on the visually-grounded “Hexagons” task and the “Text2CAD” benchmark show that code generation using a human-curated DSL yields dramatic performance gains, especially at higher abstraction levels, while automatically induced DSLs only partially recover these gains. The study highlights that the right representation can close much of the performance gap caused by abstraction, but that automatically learning such representations remains a challenge.

By Amit Lisha, Reut Tsarfaty
arXiv AI
Sep 10

LogicSkills: A Structured Benchmark for Formal Reasoning in Large Language Models

LogicSkills is a benchmark designed to isolate three core logical abilities in large language models: formal symbolization, countermodel construction, and validity assessment. The dataset draws items from the two-variable fragment of first‑order logic without identity, presented in both English and a Carrollian nonce‑word language, and all instances are solver‑verified with Z3. Results show that conventional instruction‑tuned LLMs excel at validity assessment but struggle with symbolization and countermodel construction, whereas recent reasoning‑tuned models perform well across all tasks, indicating a more systematic logical skill profile.

By Brian Rabern, Philipp Mondorf, Barbara Plank