@inproceedings{guo-etal-2025-infal,
    title = "{I}nf{AL}: Inference Time Adversarial Learning for Improving Research Ideation",
    author = "Guo, Sikun  and
      Shariatmadari, Amir Hassan  and
      Wang, Peng  and
      Huang, Albert  and
      Zhang, Aidong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.667/",
    doi = "10.18653/v1/2025.findings-emnlp.667",
    pages = "12501--12522",
    ISBN = "979-8-89176-335-7",
    abstract = "Advancements in Large Language Models (LLMs) have opened new opportunities for scientific discovery by assisting researchers in generating novel hypotheses and ideas. In this process, a major challenge is how to optimally and efficiently utilize LLMs' parametric knowledge obtained from their pretraining process. Inspired by Generative Adversarial Networks (GANs), we propose inference time adversarial learning (termed InfAL), implemented through multi-LLM-agent interactions, to enhance research ideation. This approach optimizes the utilization of LLMs' parametric knowledge without requiring additional model training, making adversarial learning efficient and context-driven. To evaluate the quality of generated ideas, we propose a relative quality ranking metric as a scalable alternative to human evaluation. Our results show that InfAL significantly improves idea generation, with GPT-4o achieving a 21{\%} increase in novelty and a 322{\%} increase in feasibility, demonstrating its transformative potential for driving innovation in scientific research."
}