Sound and Complete Neuro-symbolic Reasoning with LLM-Grounded
  Interpretations
Large language models (LLMs) have demonstrated impressive capabilities in
natural language understanding and generation, but they exhibit problems with
logical consistency in the output they generate. How can we harness LLMs'
broad-coverage parametric knowledge in formal reasoning despite their
inconsistency? We present a method for directly integrating an LLM into the
interpretation function of the formal semantics for a paraconsistent logic. We
provide experimental evidence for the feasibility of the method by evaluating
the function using datasets created from several short-form factuality
benchmarks. Unlike prior work, our method offers a theoretical framework for
neuro-symbolic reasoning that leverages an LLM's knowledge while preserving the
underlying logic's soundness and completeness properties.
6875b70a257d4f043537053c
Bradley P. Allen
6875b70a257d4f043537053d
Prateek Chhikara
6875b70a257d4f043537053e
Thomas Macaulay Ferguson
6875b70a257d4f043537053f
Filip Ilievski
6875b70a257d4f0435370540
Paul Groth
Sound and Complete Neuro-symbolic Reasoning with LLM-Grounded
  Interpretations
Large language models (LLMs) have demonstrated impressive capabilities in
natural language understanding and generation, but they exhibit problems with
logical consistency in the output they generate. How can we harness LLMs'
broad-coverage parametric knowledge in formal reasoning despite their
inconsistency? We present a method for directly integrating an LLM into the
interpretation function of the formal semantics for a paraconsistent logic. We
provide experimental evidence for the feasibility of the method by evaluating
the function using datasets created from several short-form factuality
benchmarks. Unlike prior work, our method offers a theoretical framework for
neuro-symbolic reasoning that leverages an LLM's knowledge while preserving the
underlying logic's soundness and completeness properties.
A method integrates large language models into formal semantics for paraconsistent logic, enabling neuro-symbolic reasoning while maintaining logical soundness and completeness.
large language models natural language understanding natural language generation logical consistency formal reasoning paraconsistent logic neuro-symbolic reasoning formal semantics soundness completeness