SymbolicAI: A framework for logic-based approaches combining generative
  models and solvers
Authors: name: Marius-Constantin Dinu, name: Claudiu Leoveanu-Condrei, name: Markus Holzleitner, name: Werner Zellinger, name: Sepp Hochreiter

We introduce SymbolicAI, a versatile and modular framework employing a
logic-based approach to concept learning and flow management in generative
processes. SymbolicAI enables the seamless integration of generative models
with a diverse range of solvers by treating large language models (LLMs) as
semantic parsers that execute tasks based on both natural and formal language
instructions, thus bridging the gap between symbolic reasoning and generative
AI. We leverage probabilistic programming principles to tackle complex tasks,
and utilize differentiable and classical programming paradigms with their
respective strengths. The framework introduces a set of polymorphic,
compositional, and self-referential operations for data stream manipulation,
aligning LLM outputs with user objectives. As a result, we can transition
between the capabilities of various foundation models endowed with zero- and
few-shot learning capabilities and specialized, fine-tuned models or solvers
proficient in addressing specific problems. In turn, the framework facilitates
the creation and evaluation of explainable computational graphs. We conclude by
introducing a quality measure and its empirical score for evaluating these
computational graphs, and propose a benchmark that compares various
state-of-the-art LLMs across a set of complex workflows. We refer to the
empirical score as the "Vector Embedding for Relational Trajectory Evaluation
through Cross-similarity", or VERTEX score for short. The framework codebase
and benchmark are linked below.