5 citations · 9 across the 7 of their papers we have counts for
7 papers
Semantic Bridge: Universal Multi-Hop Question Generation via AMR-Driven Graph Synthesis
Linqing Chen, Hanmeng Zhong, Wentao Wu +1
Large language model (LLM) training faces a critical bottleneck: the scarcity of high-quality, reasoning-intensive question-answer pairs, especially from sparse, domain-specific so…
Large Language Model's Multi-Capability Alignment in Biomedical Domain
Wentao Wu, Linqing Chen, Hanmeng Zhong +1
BalancedBio is a theoretically grounded framework for parameter-efficient biomedical reasoning, addressing multi-capability integration in domain-specific AI alignment. It establis…
Hierarchical Multi-Label Generation with Probabilistic Level-Constraint
Linqing Chen, Weilei Wang, Wentao Wu +1
Hierarchical Extreme Multi-Label Classification poses greater difficulties compared to traditional multi-label classification because of the intricate hierarchical connections of l…
Streamlining Biomedical Research with Specialized LLMs
Linqing Chen, Weilei Wang, Yubin Xia +30
In this paper, we propose a novel system that integrates state-of-the-art, domain-specific large language models with advanced information retrieval techniques to deliver comprehen…
CRAB: A Benchmark for Evaluating Curation of Retrieval-Augmented LLMs in Biomedicine
Hanmeng Zhong, Linqing Chen, Wentao Wu +1
Recent development in Retrieval-Augmented Large Language Models (LLMs) have shown great promise in biomedical applications. How ever, a critical gap persists in reliably evaluating…
PharmaGPT: Domain-Specific Large Language Models for Bio-Pharmaceutical and Chemistry
Linqing Chen, Weilei Wang, Zilong Bai +33
Large language models (LLMs) have revolutionized Natural Language Processing (NLP) by minimizing the need for complex feature engineering. However, the application of LLMs in speci…
PatentGPT: A Large Language Model for Intellectual Property
Zilong Bai, Ruiji Zhang, Linqing Chen +24
In recent years, large language models(LLMs) have attracted significant attention due to their exceptional performance across a multitude of natural language process tasks, and hav…