{
 "body": "\nPart of the [Arena Elo](arena_elo.md) family.\n\n## What it measures\n\narena_elo_style_control applies a style adjustment to the same votes and the same Bradley-Terry\nfit used elsewhere in the arena_elo family, rather than drawing on a different vote pool. LMSYS\nadds response length and markdown formatting (header, bold and list counts, each expressed as a\nnormalised difference between the two compared responses) as extra regressors in the logistic\nregression that produces Arena ratings, so the resulting model coefficients are adjusted for \u2014\ncontrolled for \u2014 those style effects rather than reflecting them. It can be layered onto Overall\nor onto a category such as Hard Prompts.\n\n## Reading the numbers\n\nStyle control exists because length and markdown habits measurably move the plain Arena ranking:\nin LMSYS's own analysis, controlling for both moved GPT-4o-mini and Grok-2-mini below most\nfrontier models, while Claude 3.5 Sonnet, Claude 3 Opus and Llama-3.1-405B-Instruct rose, and\nClaude 3.5 Sonnet tied for first in the Hard Prompts subset once style was controlled. A large gap\nbetween a model's plain and style-controlled rating is itself informative: it suggests a\nmeaningful share of its plain-board standing comes from formatting and verbosity rather than\nsubstance. The authors describe this as a first step, not a causal isolation of style from\nquality, since length and genuine quality (for example, a chain-of-thought explanation) can be\ncorrelated for legitimate reasons.\n",
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  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
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 },
 "disposition": {
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  "reasons": [],
  "status": "unassessed",
  "verified_results": []
 },
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 ],
 "page": {
  "aliases": [
   "Chatbot Arena Style Control",
   "LMArena Style Control",
   "Arena Score, style-controlled"
  ],
  "category": "human-preference",
  "contamination": {
   "note": "Same as the arena_elo family: prompts are live user submissions, not a fixed, publishable answer key.",
   "risk": "low"
  },
  "dataset": {
   "languages": [],
   "license": "",
   "modalities": [
    "text"
   ],
   "public_test_set": null,
   "size": null,
   "size_note": "Same live vote corpus as the arena_elo family; no separate vote count for the style-controlled fit was found from a source read for this page.",
   "splits": "",
   "url": "https://huggingface.co/lmarena-ai"
  },
  "freshness": {
   "researched": "2026-09-08",
   "researched_by": "sonnet-5 agent, batch 1b, slice J",
   "reviewed": "",
   "reviewed_by": ""
  },
  "harness": {
   "bigbench": "",
   "helm": "",
   "inspect_evals": "",
   "lm_eval": "",
   "opencompass": "",
   "other": "No offline harness; ratings come only from live votes on the platform, refit with style features included. LMSYS published a Google Colab notebook and vote/style data alongside the original analysis."
  },
  "id": "arena_elo_style_control",
  "last_updated": "2025-06",
  "leaderboard_url": "https://arena.ai/leaderboard/text",
  "lineage": {
   "family": "arena_elo",
   "predecessor": "",
   "successors": [],
   "variants": []
  },
  "measures": "arena_elo_style_control applies a style adjustment to the same votes and the same Bradley-Terry fit used elsewhere in the arena_elo family, rather than drawing on a different vote pool. LMSYS adds response length and markdown formatting (header, bold and list counts, each expressed as a normalised difference between the two compared responses) as extra regressors in the logistic regression that produces Arena ratings, so the resulting model coefficients are adjusted for \u2014 controlled for \u2014 those style effects instead of reflecting them. It can be layered onto Overall or onto a category such as Hard Prompts.\n",
  "metric": {
   "baseline_note": "Same anchored, open-ended scale as the arena_elo family; style coefficients are reported separately from the model ratings themselves.",
   "direction": "higher_is_better",
   "human_baseline": null,
   "max_score": null,
   "name": "Elo",
   "random_baseline": null,
   "unit": ""
  },
  "name": "Arena Elo \u2014 Style Control",
  "page_kind": "subset",
  "paper": {
   "arxiv": "2403.04132",
   "title": "Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference",
   "url": "https://arxiv.org/abs/2403.04132",
   "year": 2024
  },
  "publisher": {
   "authors": [
    "Tianle Li",
    "Anastasios Angelopoulos",
    "Wei-Lin Chiang"
   ],
   "org": "LMSYS (Large Model Systems Organization), UC Berkeley Sky Computing Lab (founding org); operates today as Arena (formerly LMArena)",
   "url": "https://arena.ai"
  },
  "released": "2024-08",
  "repo_url": "https://github.com/lm-sys/FastChat",
  "saturation": {
   "as_of": "",
   "note": "No current score snapshot for the style-controlled fit specifically was captured from a source read for this page; see arena_elo_overall for the current state of the unfiltered text leaderboard.",
   "status": "unknown",
   "top_score": null
  },
  "sources": [
   {
    "accessed": "2026-09-08",
    "title": "Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference (arXiv)",
    "url": "https://arxiv.org/abs/2403.04132"
   },
   {
    "accessed": "2026-09-08",
    "title": "Does style matter? Disentangling style and substance in Chatbot Arena (LMSYS blog)",
    "url": "https://www.lmsys.org/blog/2024-08-28-style-control"
   },
   {
    "accessed": "2026-09-08",
    "title": "Does Style Matter? (Arena blog, updated republication)",
    "url": "https://arena.ai/blog/style-control"
   }
  ],
  "status": "active",
  "subcategory": "pairwise human preference, style-adjusted",
  "summary": "A style-adjusted Arena ranking that regresses out response length and markdown formatting so ratings lean more on substance than presentation.",
  "tags": [
   "human-preference",
   "chatbot",
   "elo",
   "style-control"
  ],
  "task_format": "Same anonymous, randomized side-by-side text chat as the underlying category; ratings are recomputed with response-length and markdown-count features included in the regression."
 }
}