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cs.CL2026
Does Unlearning Survive Collaboration? Robust Unlearning for Shared-Model LLM Teams
Hengsong Liu, Liangyu Teng, Jingnan Cai +5
LLM unlearning is essential for removing sensitive or unwanted knowledge while preserving useful capabilities. As LLM teams are increasingly used to solve complex problems, forgett…
cs.CL2026
Marginal Response Surface Elicitation for Zero-Label Tabular Learning
Liangyu Teng, Yicheng Ding, Jing Liu +5
Tabular learning uses structured data to predict target outcomes. Traditionally, this process has relied on labeled data. However, large language models (LLMs) can be used to elici…
cs.CL2026
Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection
Liangyu Teng, Hengsong Liu, Juncen Guo +4
The performance ceiling of an LLM team is constrained not only by individual model capabilities, but also by inter-member error resonance and predictive differences. Although heter…