CPP improves generalized intent discovery by using LLM-generated prototypes and verbalizers plus consistency and cross-prediction losses, reporting SOTA on Banking and CLINC without statistical validation.
$\mathbf{{}^{12}{C} + {}^{12}{C}}$ Fusion $\boldsymbol{S^*}$-factor from a Full-microscopic Nuclear Model
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abstract
The ${}^{12}\mathrm{C} + {}^{12}\mathrm{C}$ fusion reaction plays a vital role in the explosive phenomena of the universe. The resonances in the Gamow window rule its reaction rate and products. Hence, the determination of the resonance parameters by nuclear models is indispensable as the direct measurement is not feasible. Here, for the first time, we report the resonances in the ${}^{12}\mathrm{C} + {}^{12}\mathrm{C}$ fusion reaction described by a full-microscopic nuclear model. The model plausibly reproduces the measured low-energy astrophysical $S$-factors and predicts the resonances in the Gamow window. Contradictory to the hindrance model, we conclude that there is no low-energy suppression of the $S$-factor.
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Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework
CPP improves generalized intent discovery by using LLM-generated prototypes and verbalizers plus consistency and cross-prediction losses, reporting SOTA on Banking and CLINC without statistical validation.