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When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs

Paper · 2025 · Ammar Khairi, Daniel D'souza, Ye Shen et al.

Summary

The paper asks how to best spend a small, fixed inference-compute budget to improve multilingual LLM outputs without retraining. Most prior test-time scaling work targets English and narrow domains. The authors use parallel scaling: generate several candidates, then pick one. They test it across seven languages and three generative task types (open-ended chat, math, translation), using Aya Expanse 8B, Qwen3 8B and the 111B Command A, with only five samples per prompt.

They find that non-English outputs degrade sooner as temperature rises. In response, they propose:

  • Hedged sampling, which mixes outputs drawn at varied temperatures with a greedy output.
  • CHOPS (Checklisted One-Pass Selection), a selector that uses an LLM judge.
  • X-MBR (Cross-lingual MBR), a selector that uses an LLM judge plus auxiliary samples from a dominant language.

With just five samples, these methods raise m-ArenaHard v2.0 win rates by roughly 9–17 points over single-sample decoding. They often beat Best-of-N with dedicated reward models. The work suggests that generalist multilingual judges can make cheap inference-time gains available beyond English.

Key points

  • Evaluation covers m-ArenaHard v2.0 win rates judged by GPT-4o, against greedy decoding and gemini-2.0-flash. It also covers MGSM accuracy and WMT translation from English scored with XComet-XL, using separate dev and test splits.
  • Win-rate gains over single samples are +17.3% for Aya Expanse 8B, +9.4% for Qwen3 8B and +9.0% for Command A. The 8B models gain an average of +6.8 against Gemini on non-English prompts.
  • In one reported non-English comparison, CHOPS improved by +10.7% and X-MBR by +10.4%, versus +7.5% for reward-model BoN.
  • The proposed "Multilingual LLMonade Recipe" has two steps. First, generate candidates with hedged sampling, with temperatures starting around 0.7–0.9. Second, choose among them with a multilingual LLM judge via CHOPS or X-MBR.
  • Limitations: all seven languages are high-resource. The large judge model also adds substantial cost.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢CohereSan Francisco · United States15 works · 1,223 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksMultilingual & cross-lingual NLP · 828 works

Cite

Cite as Ammar Khairi, Daniel D'souza, Ye Shen, Julia Kreutzer and Sara Hooker. 2025. When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.emnlp-main.1402

@inproceedings{khairi2025when,
  title = {{When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs}},
  author = {Ammar Khairi, Daniel D'souza, Ye Shen, Julia Kreutzer and Sara Hooker},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.1402},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.1402}
}

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