@inproceedings{brunello-etal-2025-blackboxnlp,
    title = "{B}lackbox{NLP}-2025 {MIB} Shared Task: {IPE}: Isolating Path Effects for Improving Latent Circuit Identification",
    author = "Brunello, Nicol{\`o}  and
      Cerutti, Andrea  and
      Sassella, Andrea  and
      Carman, Mark James",
    editor = "Belinkov, Yonatan  and
      Mueller, Aaron  and
      Kim, Najoung  and
      Mohebbi, Hosein  and
      Chen, Hanjie  and
      Arad, Dana  and
      Sarti, Gabriele",
    booktitle = "Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.blackboxnlp-1.30/",
    pages = "528--536",
    ISBN = "979-8-89176-346-3",
    abstract = "Understanding why large language models (LLMs) exhibit certain behaviors is the goal of mechanistic interpretability. One of the major tools employed by mechanistic interpretability is circuit discovery, i.e., identifying a subset of the model{'}s components responsible for a given task. We present a novel circuit discovery technique called IPE (Isolating Path Effects) that, unlike traditional edge-centric approaches, aims to identify entire computational paths (from input embeddings to output logits) responsible for certain model behaviors. Our method modifies the messages passed between nodes along a given path in such a way as to either precisely remove the effects of the entire path (i.e., ablate it) or to replace the path{'}s effects with those that would have been generated by a counterfactual input. IPE is different from current path-patching or edge activation-patching techniques since they are not ablating single paths, but rather a set of paths sharing certain edges, preventing more precise tracing of information flow. We apply our method to the well-known Indirect Object Identification (IOI) task, recovering the canonical circuit reported in prior work. On the MIB workshop leaderboard, we tested IOI and MCQA tasks on GPT2-small and Qwen2.5. For GPT2, path counterfactual replacement outperformed path ablation as expected and led to top-ranking results, while for Qwen, no significant differences were observed, indicating a need for larger experiments to distinguish the two approaches."
}