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cs.LG2026
When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning
Chenghao Qiu, Chunli Peng, Yufeng Yang +2
In-context learning (ICL) is often motivated by the intuition that demonstrations help because they provide correct input-output examples. However, we reveal a counterintuitive phe…
cs.LG2025
Adaptive Helpfulness-Harmlessness Alignment with Preference Vectors
Ren-Wei Liang, Chin-Ting Hsu, Chan-Hung Yu +6
Ensuring that large language models (LLMs) are both helpful and harmless is a critical challenge, as overly strict constraints can lead to excessive refusals, while permissive mode…