Recent approaches have explored language-guided classifiers capable of
classifying examples from novel tasks when provided with task-specific natural
language explanations, instructions or prompts (Sanh et al., 2022; R. Menon et
al., 2022). While these classifiers can generalize in zero-shot settings, their
task performance often varies substantially between different language
explanations in unpredictable ways (Lu et al., 2022; Gonen et al., 2022). Also,
current approaches fail to leverage unlabeled examples that may be available in
many scenarios. Here, we introduce TALC, a framework that uses data programming
to adapt a language-guided classifier for a new task during inference when
provided with explanations from multiple teachers and unlabeled test examples.
Our results show that TALC consistently outperforms a competitive baseline from
prior work by an impressive 9.3% (relative improvement). Further, we
demonstrate the robustness of TALC to variations in the quality and quantity of
provided explanations, highlighting its potential in scenarios where learning
from multiple teachers or a crowd is involved. Our code is available at:
this https URL

介绍了一种使用数据编程的框架 TALC，该框架利用来自多个教师的解释和未标记的测试样本，在推理过程中对新任务的语言引导分类器进行自适应。结果表明 TALC 在相对改进方面始终优于先前工作中的竞争基线达 9.3％。此外，我们展示了 TALC 对提供的解释质量和数量变化的鲁棒性，凸显了它在涉及多个教师或群体学习的场景中的潜力。