Bin Liu

Duorong Wang, Xiaoting Wei, Bin Liu et al.

Open-source model platforms have made it easier to publish AI models, but model release alone does not reveal whether models become visible, technically reused, or incorporated into public applications. This study introduces public application transformation as a platform-visible dimension of model impact and examines it through structured Model-Space links on Hugging Face. We construct a platform-scale dataset of 2.56 million model repositories, 1.06 million Spaces, 810,087 dataset repositories, and 1.22 million account profiles, together with Model-Space, Dataset-Space, and model-to-model technical reuse links. The analysis shows that public application transformation is highly selective and concentrated: only a small share of models are linked to Spaces, and most Model-Space links are concentrated among a limited set of models. More importantly, application transformation is associated with platform visibility but is not equivalent to technical reuse, indicating that downloads, likes, downstream model reuse, and application-facing uptake capture different forms of model impact. Additional analyses show that application-transformed models tend to exhibit stronger metadata-based readiness and enter heterogeneous Space configurations involving datasets, SDKs, and task-specific application categories. By tracing how models move from repositories into public applications and demos, this study extends the measurement of open-source model impact from artifact availability and technical reuse to platform-mediated transformation across AI information objects.

Guosheng Li, Fenghui Ren, Bin Liu et al.

Climate disclosure classification is a fundamental task for analysing corporate climate disclosures, yet such disclosures appear in many different sources -- annual reports, press releases, and earnings calls -- that differ in length, purpose, and writing style. Existing evaluations are mostly conducted within a single source, leaving open whether common LLM adaptation strategies remain effective under source shift. We reframe climate disclosure classification as a cross-source adaptation problem and study three widely used adaptation strategies -- definitions, examples, and fine-tuning -- across eleven open- and closed-source LLMs, using two corpora that share the same label space but come from different sources. We find that all strategies bring positive cross-source gains on average, but the strongest in-source strategies are not the strongest cross-source ones: similarity-based retrieval and LoRA fine-tuning gain most in-source but lose most of that advantage under source shift; randomly selected few-shot examples, a weaker in-source baseline, retain their advantage more reliably; definitions transfer most consistently, though only when their granularity matches the target text. Across these strategies, when the source changes, simpler is often safer.