@inproceedings{cohen-etal-2026-dfpe,
    title = "{DFPE}: A Diverse Fingerprint Ensemble for Enhancing {LLM} Performance",
    author = "Cohen, Seffi  and
      Inger, Nurit Cohen  and
      Goldshlager, Niv  and
      Shapira, Bracha  and
      Rokach, Lior",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.282/",
    pages = "5326--5336",
    ISBN = "979-8-89176-386-9",
    abstract = "Large Language Models (LLMs) demonstrate impressive capabilities but exhibit inconsistent performance across diverse domains. We propose DFPE (Diverse Fingerprint Ensemble), a novel training-free method that systematically constructs subject-adaptive ensembles by balancing model diversity and competence. DFPE introduces three key innovations: (1) semantic fingerprinting using averaged response embeddings to capture distinct problem-solving patterns, (2) DBSCAN-based clustering with quantile-based competence filtering to ensure diverse yet capable model selection, and (3) exponentially-weighted aggregation adapted to subject-specific performance. Our method{'}s effectiveness is highlighted on the challenging MMLU-pro benchmark, where DFPE achieves a striking 17.1 percentage point gain over the best single model, reaching 71.4{\%} accuracy. This strong performance is consistent across other standard benchmarks, with significant accuracy improvements of 4.4 points on AGIEval and 2.7 points on MMLU. Our results underscore that a systematic approach to ensemble construction - one that balances diversity, subject-specific competence, and adaptive weighting, can substantially enhance the generalization and robustness of LLMs on multifaceted language understanding tasks."
}