Cross-entropy comparison of LLM responses reveals Kimi's similarity to Claude
You're relatively right!Which LLM models write alike? 0) + 1); } return { map, n: Math.max(1, text.length - k + 1) }; } function countList(items: string[]): Counts { const map = new Map<string, number>(); for (const it of items) map.set(it, (map.get(it) ?? 0) + 1); return { map, n: Math.max(1, items.length) }; } function pool(all: Counts[]): Counts { const map = new Map<string, number>(); let n = 0; for (const c of all) { for (const [g, v] of c.map) map.set(g, (map.get(g) ??
- ▪You're relatively right!Which LLM models write alike?
- ▪0) + 1); } return { map, n: Math.max(1, text.length - k + 1) }; } function countList(items: string[]): Counts { const map = new Map<string, number>(); for (const it of items) map.set(it, (map.get(it) ??
- ▪0) + 1); return { map, n: Math.max(1, items.length) }; } function pool(all: Counts[]): Counts { const map = new Map<string, number>(); let n = 0; for (const c of all) { for (const [g, v] of c.map) map.set(g, (map.get(g) ??
Opening excerpt (first ~120 words) tap to expand
You're relatively right!Which LLM models write alike? A heat map built from their words alone.--- format: typebulb/v1 name: "You're relatively right!" --- **code.tsx** ```tsx import React, { useEffect, useMemo, useState } from "react"; import { createRoot } from "react-dom/client"; import { encode } from "gpt-tokenizer/encoding/cl100k_base"; // ── Types ──────────────────────────────────────────────────────── type RawResult = { testId: string; testName: string; subject: string; score: number; response: string; response2?: string; reasoning: string; error?: string; }; type RawData = { timestamp?: string; judge?: string; results: RawResult[] }; type MetaRow = { match: string; lab: string; released: string | null }; type Counts = { map: Map<string, number>; n: number }; type Bg = { words:…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Typebulb.
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