2 papers
cs.CL2025
Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning
Hyundong Cho, Karishma Sharma, Nicolaas Jedema +4
Language models are aligned to the collective voice of many, resulting in generic outputs that do not align with specific users' styles. In this work, we present Trial-Error-Explai…
hep-ph2023
The framework of the auxiliary group: two birds with one stone
R. Krishnan
Flavon models in the literature assume constraints on the components of the vacuum expectation values (vevs) of flavons, and typically, these constraints are not fully determined b…