bibtext: @inproceedings{
  hennigen2024towards,
  title={Towards Verifiable Text Generation with Symbolic References},
  author={Lucas Torroba Hennigen and Zejiang Shen and Aniruddha Nrusimha and Bernhard Gapp and David Sontag and Yoon Kim},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=fib9qidCpY}
  }
abstract: 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 exper- iments, 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.
title: Towards Verifiable Text Generation with Symbolic References
authors: Lucas Torroba Hennigen Zejiang Shen Aniruddha Nrusimha Bernhard Gapp David Sontag Yoon Kim