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    <abstract>Recent developments in the quality and accessibility of large language models have precipitated a surge in user-facing tools for content generation. Motivated by a necessity for human quality control of these systems, we introduce ReportGPT: a pipeline framework for verifiable human-in-the-loop table-to-text generation. ReportGPT is based on a domain specific language, which acts as a proof mechanism for generating verifiable commentary. This allows users to quickly check the relevancy and factuality of model outputs. User selections then become few-shot examples for improving the performance of the pipeline. We configure 3 approaches to our pipeline, and find that usage of language models in ReportGPT’s components trade off precision for more insightful downstream commentary. Furthermore, ReportGPT learns from human feedback in real-time, needing only a few samples to improve performance.</abstract>
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%0 Conference Proceedings
%T ReportGPT: Human-in-the-loop Verifiable Table-to-Text Generation
%A Cecchi, Lucas
%A Babkin, Petr
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F cecchi-babkin-2024-reportgpt
%X Recent developments in the quality and accessibility of large language models have precipitated a surge in user-facing tools for content generation. Motivated by a necessity for human quality control of these systems, we introduce ReportGPT: a pipeline framework for verifiable human-in-the-loop table-to-text generation. ReportGPT is based on a domain specific language, which acts as a proof mechanism for generating verifiable commentary. This allows users to quickly check the relevancy and factuality of model outputs. User selections then become few-shot examples for improving the performance of the pipeline. We configure 3 approaches to our pipeline, and find that usage of language models in ReportGPT’s components trade off precision for more insightful downstream commentary. Furthermore, ReportGPT learns from human feedback in real-time, needing only a few samples to improve performance.
%U https://aclanthology.org/2024.emnlp-industry.39
%P 529-537

Markdown (Informal)

[ReportGPT: Human-in-the-loop Verifiable Table-to-Text Generation](https://aclanthology.org/2024.emnlp-industry.39) (Cecchi & Babkin, EMNLP 2024)

• ReportGPT: Human-in-the-loop Verifiable Table-to-Text Generation (Cecchi & Babkin, EMNLP 2024)

ACL

• Lucas Cecchi and Petr Babkin. 2024. ReportGPT: Human-in-the-loop Verifiable Table-to-Text Generation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 529–537, Miami, Florida, US. Association for Computational Linguistics.