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cs.CL2026
Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels
Junjie Ye, Yuming Yang, Yang Nan +7
Large language models (LLMs) acquire substantial world knowledge during pre-training, which is further shaped by post-training techniques such as supervised fine-tuning (SFT). Howe…
cs.CL2025
Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric
Yuming Yang, Yang Nan, Junjie Ye +8
Data diversity is crucial for the instruction tuning of large language models. Existing studies have explored various diversity-aware data selection methods to construct high-quali…
cs.CL2025
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition
Yuming Yang, Wantong Zhao, Caishuang Huang +11
Open Named Entity Recognition (NER), which involves identifying arbitrary types of entities from arbitrary domains, remains challenging for Large Language Models (LLMs). Recent stu…