MultiLexNorm++ (LLM Pipeline) - Japanese: leaderboard
Lexical normalization of Japanese social-media text, a set newly annotated for MultiLexNorm++ (Buaphet et al., 2026). Error reduction rate in percent: correct minus wrong normalizations over the words that need normalization, so 0 equals leaving the text unchanged and a negative score means more wrong than right edits; here an 8-shot LLM normalizes the words an XLM-R detector flags, after a lookup in a lexicon built from the training data, mean of three runs, and higher is better.
Metric: Error reduction rate (%; (correct minus wrong normalizations) / words that need normalization, so 0 equals leaving the text unchanged and a negative score means more wrong than right edits; the floor is -100 divided by the share of words needing normalization; 8-shot prompting, mean of three runs; the LLM normalizes the words an XLM-R detector flags after a training-lexicon lookup; the new Japanese social-media set, 7.03% of words normalized). Source: arxiv.org. Saturation forecast: Estimated already saturated. 4 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-4o | 22.52 | #741 |
| 2 | Qwen 2.5 72B Instruct | 20.34 | #1093 |
| 3 | Llama 3.3 70B Instruct | 16.27 | #1353 |
| 4 | Gemma 3 27B (IT) | 14.66 | #1306 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=multilexnorm-plus-plus-llm-pipeline-japanese · How It Works · Data refreshed daily, snapshot 2026-11-12.