LLM-Guided Weighted Contrastive Learning with Topic-Aware Masking for Efficient Domain Adaptation: A Case Study on Pulp-Era Science Fiction

Domain adaptation of pre-trained language models remains challenging, especially for specialized text collections that include distinct vocabularies and unique semantic structures. Existing contrastive learning methods frequently rely on generic masking techniques and coarse-grained similarity measures, which limit their ability to capture fine-grained, domain-specific linguistic nuances. This paper proposes an enhanced domain adaptation framework by integrating weighted contrastive learning guided by large language model (LLM) feedback and a novel topic-aware masking strategy. Specifically, topic modeling is utilized to systematically identify semantically crucial domain-specific terms, enabling the creation of meaningful contrastive pairs through three targeted masking strategies: single-keyword, multiple-keyword, and partial-keyword masking. Each masked sentence undergoes LLM-guided reconstruction, accompanied by graduated similarity assessments that serve as continuous, fine-grained supervision signals. Experiments conducted on an early 20th-century science fiction corpus demonstrate that the proposed approach consistently outperforms existing baselines, such as SimCSE and DiffCSE, across multiple linguistic probing tasks within the newly introduced SF-ProbeEval benchmark. Furthermore, the proposed method achieves these performance improvements with significantly reduced computational requirements, highlighting its practical applicability for efficient and interpretable adaptation of language models to specialized domains.