---
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.