SymbolicAI: A framework for logic-based approaches combining generative
  models and solvers
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.
65bc6c70980f5719ccf39ae5
Marius-Constantin Dinu
admin_assigned
2024-02-02T11:40:09.412Z
62cd7fc2a9be5c19555caa11
/avatars/b0b3e38942360dfc7329b9eb4181fa3d.svg
Marius-Constantin Dinu
user
Xpitfire
65bc6c70980f5719ccf39ae6
Claudiu Leoveanu-Condrei
claimed_verified
2024-02-03T10:48:26.912Z
63692f9a7c5dd0caa7d54bdf
/avatars/90c38fcc055da47e1f4712deaf8b4537.svg
Leoveanu-Condrei Claudiu
user
futurisold
65bc6c70980f5719ccf39ae7
Markus Holzleitner
65bc6c70980f5719ccf39ae8
Werner Zellinger
65bc6c70980f5719ccf39ae9
Sepp Hochreiter
SymbolicAI: A framework for logic-based approaches combining generative
  models and solvers
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.
SymbolicAI integrates generative models with solvers using LLMs as semantic parsers, enabling symbolic reasoning and generative AI tasks via probabilistic and differentiable programming.
SymbolicAI logic-based approach concept learning flow management generative processes large language models semantic parsers natural language formal language probabilistic programming differentiable programming polymorphic operations compositional operations self-referential operations data stream manipulation foundation models zero-shot learning few-shot learning specialized solvers computational graphs explainable computational graphs VERTEX score Vector Embedding for Relational Trajectory Evaluation through Cross-similarity