@inproceedings{singh-yannakoudakis-2025-shot,
    title = "Few-Shot Open-Set Classification via Reasoning-Aware Decomposition",
    author = "Singh, Avyav Kumar  and
      Yannakoudakis, Helen",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.emnlp-main.699/",
    doi = "10.18653/v1/2025.emnlp-main.699",
    pages = "13865--13886",
    ISBN = "979-8-89176-332-6",
    abstract = "Large language models (LLMs) excel at few-shot learning, but their ability to reject out-of-distribution examples remains under-explored. We study this challenge under the setting of few-shot open-set classification, where a model must not only classify examples from a small set of seen classes but also reject unseen ones at inference time. This setting is more realistic and challenging than traditional closed-set supervised learning, requiring both fine-grained classification and robust rejection. We show that, for small LLMs, neither chain-of-thought (CoT) prompting nor supervised fine-tuning (SFT) alone are sufficient to generalise reliably, particularly when class semantics are anonymised. We introduce Wasserstein GFN (W-GFN), a novel amortised Generative Flow Network framework that uses latent trajectories to approximate the Bayesian posterior. With as few as 4 examples per class, W-GFN substantially improves performance, enabling Llama 3.2 3B to achieve up to $\geq80\%$ of the performance of Llama 3.3 70B in complex datasets, despite being $\sim 23$ times smaller, which highlights the importance of reasoning-aware approaches for robust open-set few-shot learning."
}