arXiv AI
From Confusion to Clarity: Confusion-Aware Retrieval and Knowledge Injection for Text Classification
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LFM: Leveraging Foundation Models for Source-Free Universal Domain Adaptation
arXiv:2607. 17653v1 Announce Type: cross Abstract: Source-free universal domain adaptation (SF-UniDA) adapts a pre-trained source model to an unlabeled target domain under both covariate and label shifts, without access to source data.
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KItCAT: Knowledge Injection via Input Corruption for Auto-regressive Training
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1d ago
BioELX: Context-Aware Cross-lingual Biomedical Entity Linking without Task-Specific Supervision
BioELX is a retrieve‑rerank framework for cross‑lingual biomedical entity linking that tackles two key problems: the English‑biased UMLS alias training data and the degradation caused by naïvely adding context. It fine‑tunes SapBERT_multi with Wikidata‑derived cross‑lingual alias supervision to create shared concept neighborhoods, and then reranks candidates using pretrained LLMs with mention‑anchored prompting to focus on the target mention. Experiments demonstrate state‑of‑the‑art performance on four benchmarks, improving Recall@1 by 4.8–18.2 percentage points without task‑specific annotations.