title: SymbolicAI: A framework for logic-based approaches combining generative
    models and solvers
summary: 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.
paper_authors._id: 65bc6c70980f5719ccf39ae5
paper_authors.name: Marius-Constantin Dinu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-02T11:40:09.412Z
paper_authors._id: 62cd7fc2a9be5c19555caa11
paper_authors.avatarUrl: /avatars/b0b3e38942360dfc7329b9eb4181fa3d.svg
paper_authors.fullname: Marius-Constantin Dinu
paper_authors.type: user
paper_authors.user: Xpitfire
paper_authors._id: 65bc6c70980f5719ccf39ae6
paper_authors.name: Claudiu Leoveanu-Condrei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-03T10:48:26.912Z
paper_authors._id: 63692f9a7c5dd0caa7d54bdf
paper_authors.avatarUrl: /avatars/90c38fcc055da47e1f4712deaf8b4537.svg
paper_authors.fullname: Leoveanu-Condrei Claudiu
paper_authors.type: user
paper_authors.user: futurisold
paper_authors._id: 65bc6c70980f5719ccf39ae7
paper_authors.name: Markus Holzleitner
paper_authors._id: 65bc6c70980f5719ccf39ae8
paper_authors.name: Werner Zellinger
paper_authors._id: 65bc6c70980f5719ccf39ae9
paper_authors.name: Sepp Hochreiter
paper_title: SymbolicAI: A framework for logic-based approaches combining generative
    models and solvers
paper_summary: 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.
paper_ai_keywords: logic-based approach concept learning flow management generative processes symbolicAI semantic parsers natural language formal language symbolic reasoning generative AI probabilistic programming differentiable programming classical programming polymorphic operations compositional operations self-referential operations data stream manipulation zero-shot learning few-shot learning specialized models solvers explainable computational graphs Vector Embedding for Relational Trajectory Evaluation through Cross-similarity (VERTEX)