Making LLMs Reason? The Intermediate Language Problem in Neurosymbolic Approaches

Logical reasoning tasks manifest themselves as a challenge to Large Language Models (LLMs). Neurosymbolic approaches use LLMs to translate logical reasoning problems formulated in natural language into a formal intermediate language. Subsequently, the usage of symbolic reasoners yields reliable solving thereof. However, LLMs often fail in translation due to poorly chosen intermediate languages. We introduce the intermediate language problem, which is the problem of choosing a suitable formal language representation for neurosymbolic approaches. Theoretically, we argue that its origins lie in the inability of LLMs to distinguish syntax from semantics and the relative independence of the problem from its representation. We showcase its existence experimentally by contrasting two intermediate languages, Answer Set Programming and the Python Knowledge Engine. In addition, we demonstrate the effects of varying degrees of supplementary context information. Our results show a maximum difference in overall-accuracy of 53.20% and 49.26% in execution-accuracy. When using the GPT4o-mini LLM we beat the state-of-the-art in overall-accuracy on the ProntoQA dataset by 21.20% and by 50.50% on the ProofWriter dataset.

ASP-driven User-interaction with Clinguin

We present clinguin, a system for ASP-driven user interface design. Clinguin streamlines the development of user interfaces for ASP developers by letting them build interactive prototypes directly in ASP, eliminating the need for separate frontend languages. To this end, clinguin uses a few dedicated predicates to define user interfaces and the treatment of user-triggered events. This simple design greatly facilitates the specification of user interactions with an ASP system, in our case clingo.