  LLMs are vulnerable to hallucinations, and thus their outputs generally
require laborious human verification for high-stakes applications. To this end,
we propose symbolically grounded generation (SymGen) as a simple approach for
enabling easier manual validation of an LLM's output. SymGen prompts an LLM to
interleave its regular output text with explicit symbolic references to fields
present in some conditioning data (e.g., a table in JSON format). The
references can be used to display the provenance of different spans of text in
the generation, reducing the effort required for manual verification. Across a
range of data-to-text and question-answering experiments, we find that LLMs are
able to directly output text that makes use of accurate symbolic references
while maintaining fluency and factuality. In a human study we further find that
such annotations can streamline human verification of machine-generated text.
Our code will be available at http://symgen.github.io.
