Speaker
Description
We propose CG-SFT, a parameter-efficient framework that turns a causal large language model into a structured hierarchical multi-label classifier without adding a classification head. The label space is encoded as a fixed sequence where each label is followed by a <yes>/<no> decision token. Supervision is applied only to these decision positions using cross-entropy, binary cross-entropy, and Dice losses, plus a hierarchy penalty. New decision tokens are initialized with lightweight adapters and combined with LoRA updates on Qwen2.5-7B. We evaluate on 1,819 ML4HEP paper abstracts annotated with a 91‑label taxonomy (77 parent–child relations, average 2.8 positives per paper). Exact‑match accuracy (all 91 decisions correct) is used as the primary metric. On the held‑out test set (186 papers), CG‑SFT achieves 60.2% exact match, compared to 1.1% for the unfine‑tuned backbone and 21.0% for GPT‑5.4 Max—gains of 59.1 and 39.2 percentage points, respectively. Training takes about one hour on a single A800 GPU. Our approach bridges generative fine‑tuning and discriminative classification, enabling scalable curation and retrieval of scientific literature with sparse hierarchical labels. Furthermore, the curated data can be distilled into design knowledge for ML models in particle physics, laying the groundwork for future agent-assisted model development.