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cs.CL2026
Select, Don't Train: The Benefits of Modular Entity Disambiguation with LLM-Based Selection
Fina Polat, Daniel Daza, Pengyu Zhang +2
Entity Disambiguation (ED) is a key task for constructing and using knowledge graphs. State-of-the-art neural approaches commonly model ED as a single task, although it consists of…
cs.CL2026
Fusion Training for Mathematical Generalization in Large Language Models
Congfeng Cao, Pengyu Zhang, Jelke Bloem
Thinking Mode Fusion (TMF) enables large language models to support both concise responses and long-form reasoning by unifying a non-thinking mode and a thinking mode within a sing…