  Training or finetuning large-scale language models (LLMs) requires
substantial computation resources, motivating recent efforts to explore
parameter-efficient adaptation to downstream tasks. One approach is to treat
these models as black boxes and use forward passes (Inference APIs) to interact
with them. Current research focuses on adapting these black-box models to
downstream tasks using gradient-free prompt optimization, but this often
involves an expensive process of searching task-specific prompts. Therefore, we
are motivated to study black-box language model adaptation without prompt
search. Specifically, we introduce a label-enhanced cross-attention network
called CrossTune, which models the semantic relatedness between the input text
sequence and task-specific label descriptions. Its effectiveness is examined in
the context of few-shot text classification. To improve the generalization of
CrossTune, we utilize ChatGPT to generate additional training data through
in-context learning. A switch mechanism is implemented to exclude low-quality
ChatGPT-generated data. Through extensive experiments on seven benchmark text
classification datasets, we demonstrate that our proposed approach outperforms
the previous state-of-the-art gradient-free black-box tuning method by 5.7% on
average. Even without using ChatGPT-augmented data, CrossTune performs better
or comparably than previous black-box tuning methods, suggesting the
effectiveness of our approach.
