Reliable Gradient-free and Likelihood-free Prompt Tuning
This work addresses a practical challenge for users of commercial or privacy-restricted PLMs by enabling effective adaptation without internal model access, though it builds incrementally on existing prompt tuning techniques.
The paper tackles the problem of fine-tuning large pre-trained language models (PLMs) when only black-box API access is available, by developing gradient-free and likelihood-free prompt tuning methods that learn a distribution over prompts to quantify uncertainty, achieving competitive or improved performance compared to gradient-based approaches with full model access.
Due to privacy or commercial constraints, large pre-trained language models (PLMs) are often offered as black-box APIs. Fine-tuning such models to downstream tasks is challenging because one can neither access the model's internal representations nor propagate gradients through it. This paper addresses these challenges by developing techniques for adapting PLMs with only API access. Building on recent work on soft prompt tuning, we develop methods to tune the soft prompts without requiring gradient computation. Further, we develop extensions that in addition to not requiring gradients also do not need to access any internal representation of the PLM beyond the input embeddings. Moreover, instead of learning a single prompt, our methods learn a distribution over prompts allowing us to quantify predictive uncertainty. Ours is the first work to consider uncertainty in prompts when only having API access to the PLM. Finally, through extensive experiments, we carefully vet the proposed methods and find them competitive with (and sometimes even improving on) gradient-based approaches with full access to the PLM.