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Whose Voice Survives the Summary? A Voice-Retention Audit of LLM Employee Listening

2026-09-30 02:45:08
Thilo Tamme, Anton Hantel, Bijan Khosrawi-Rad
arXiv_AI
           
Abstract

Organizations increasingly route employee feedback to leaders through large language model (LLM) summaries, an unaudited layer that silences already-spoken voice. We introduce a Voice Retention / Representation Ratio metric for representational bias in summarization and apply it to a bilingual (English/German) corpus of 2,586 free-text responses from a global professional service company. First, employees supply criticism more reliably than praise (withholding praise is 82 times more common). Second, across 45 leader-summaries the pipeline filters by popularity, not sentiment: criticism survives, yet a concern voiced once is dropped 86% of the time, with short and German-only content lost on the same axis (theme retention 0.14 vs 0.74; German directional). Controlling for frequency, sentiment has no independent effect; the harm is prevalence-driven, which sentiment-only audits miss. A targeted prompt recovers only named themes. We contribute the metric, field evidence, and a disaggregated voice-retention card.

Abstract (translated)

URL

https://arxiv.org/abs/2609.38818

PDF

https://arxiv.org/pdf/2609.38818.pdf

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