标量控制文本生成中外推的实证研究
An Empirical Study of Extrapolation in Text Generation with Scalar Control
摘要：
我们进行了实证评估外推性能条件标量控制输入时，如所需的输出长度，所需的编辑从输入句子，并在三个文本生成任务所需的情绪。具体来说，我们检查了一个zero-shot设置，其中模型被要求概括为训练期间未看到的控制值范围。我们专注于评估标量输入的流行嵌入方法，包括可学习和正弦嵌入，以及更简单的方法。令人惊讶的是，我们的研究结果表明，直接使用标量输入的最简单策略，而无需进一步编码，最可靠地允许成功的外推。
英文摘要：
We conduct an empirical evaluation of extrapolation performance when conditioning on scalar control inputs like desired output length, desired edit from an input sentence, and desired sentiment across three text generation tasks. Specifically, we examine a zero-shot setting where models are asked to generalize to ranges of control values not seen during training. We focus on evaluating popular embedding methods for scalar inputs, including both learnable and sinusoidal embeddings, as well as simpler approaches. Surprisingly, our findings indicate that the simplest strategy of using scalar inputs directly, without further encoding, most reliably allows for successful extrapolation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
