On Improving Faithfulness of Podcasts from Documents

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[Submitted on 24 Jul 2026]

Title:On Improving Faithfulness of Podcasts from Documents

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Abstract:Large language models (LLMs) are increasingly used to generate long-form conversational content such as podcasts from textual sources. While these systems produce fluent and engaging narratives, they often introduce ungrounded information. In this work, we present the first systematic study of faithfulness in document-grounded podcast generation, where grounding must be maintained across conversational turns in long-form, multi-speaker transcripts. We construct a dataset of over 1500 documents spanning five domains and generate podcast transcripts using multiple LLMs. We introduce a turn-level LLM-as-a-judge framework for evaluating whether conversational turns are supported by the source document, and validate its reliability through human studies. Our analysis shows that even state-of-the-art models, including GPT-4o, frequently generate ungrounded content. To mitigate this issue, we propose catch-n-repair, a model-agnostic framework that detects and rewrites unfaithful conversational turns while preserving conversational flow. Experiments demonstrate consistent improvements in faithfulness across both in-domain and out-of-domain settings.

Comments: Under Submission

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2607.21961 [cs.CL]

(or arXiv:2607.21961v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.21961

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Soumya Dutta Mr [view email] [v1] Fri, 24 Jul 2026 04:17:56 UTC (407 KB)

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