titleRaguTeam at SemEval-2026 Task 8: Meno and Friends in a Judge-Orchestrated LLM Ensemble for Faithful Multi-Turn Response Generation
authorsIvan Bondarenko, Roman Derunets, Oleg Sedukhin, Mikhail Komarov, Ivan Chernov, Mikhail Kulakov
date2026-05-06
venuearXiv (preprint, not peer reviewed)
idarXiv:2605.04523
affiliationNovosibirsk State University
upvotes46 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractWe present our winning system for Task~B (generation with reference passages) in SemEval-2026 Task~8: MTRAGEval. Our method is a heterogeneous ensemble of seven LLMs with two prompting variants, where a GPT-4o-mini judge selects the best candidate per instance. We ranked 1st out of 26 teams, achieving a conditioned harmonic mean of 0.7827 and outperforming the strongest baseline (gpt-oss-120b, 0.6390). Ablations show that diversity in model families, scales, and prompting strategies is essential, with the ensemble consistently beating any single model. We also introduce Meno-Lite-0.1, a 7B domain-adapted model with a strong cost--performance trade-off, and analyse MTRAGEval, highlighting annotation limitations and directions for improvement. Our code is publicly available: https://github.com/RaguTeam/ragu_mtrag_semeval

cite

@misc{bondarenko2026raguteam,
  title={RaguTeam at SemEval-2026 Task 8: Meno and Friends in a Judge-Orchestrated LLM Ensemble for Faithful Multi-Turn Response Generation},
  author={Ivan Bondarenko and Roman Derunets and Oleg Sedukhin and Mikhail Komarov and Ivan Chernov and Mikhail Kulakov},
  year={2026},
  eprint={2605.04523},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2605.04523}
}