Our method is applicable to all NMT models. It does not require extra monolingual data like back-translation, nor does it add more computations and parameters like ensembles ofmodels.

Our findings reveal that: I)LLMs with higher accuracy may exhibit lower certainty; II) Larger-scale LLMs may display greater uncertainty compared to their smaller counterparts; and III)Instruction-finetuning tends to increase the uncertainty of LLMs. These results underscore the significance of incorporating uncertainty into the evaluation of LLMs.

Figure 2 compares Figure 3(a) gir.

Network outputs can change indirectly to unexpected values after any random batch update for input data not included in the batch, called churn in this paper.

Deep learning is booming driven by massive labeled data over the past few years, such as image classification[1,2],semanticsegmentation[3,4],objectdetection[5,6],naturallanguageprocessing [7,8].

Gradient Descent (GD) is a simple yet efficient baseline algorithm for solving nonconvex optimization problems.