A newer version of the Gradio SDK is available: 6.14.0
metadata
title: BeigificationBench
emoji: 📊
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 6.10.0
app_file: app.py
pinned: false
license: mit
BeigificationBench
An anonymous benchmark evaluating how large language models flatten and homogenize text during rewriting — a phenomenon we call beigification.
What is Beigification?
Beigification describes the tendency of LLMs to produce safe, bland, stylistically uniform rewrites that strip out the distinctive voice, specificity, and informational density of source texts.
Metrics
- Lossiness — NLI-weighted information loss (proposition loss + semantic distance + word deletion)
- Drift — Model collapse indicator combining spiciness loss and centroid pull
- Spiciness — 6-component measure of textual vividness (perplexity, lexical richness, rare word density, word specificity, vivid modifier ratio, voice score)
- NLI Retention — Proportion of source propositions preserved in the rewrite
Benchmark Design
Single-hop results are averaged across 3 independent replicates to reduce variance. Multi-hop results show degradation trajectories over 8 successive rewrites.
Submitted for anonymous peer review.