Gradient-Free Prompt Tuning

Gradient-Free and Likelihood-Free Prompt Tuning is a novel method for adapting black-box language models without requiring access to their internal representations, gradients, or likelihood scores. This approach is particularly useful when working with large pre-trained language models (PLMs), which are often available only via APIs that restrict access to internal data due to privacy or commercial constraints.

While prompt tuning is already more efficient than full fine-tuning, it usually relies on gradient-based optimization. This means that you need access to the model's internal gradients and likelihood scores, which isn’t possible with many black-box models available via APIs.

Comparison with Previous Approaches

Technique	Gradient-Free?	Likelihood-Free?	Uncertainty Quantification?	Access to Internal Representations?
Fine-Tuning	❌	❌	❌	✅
Prompt Tuning (Soft)	❌	❌	❌	✅
BBT (Black-Box Tuning)	✅	❌	❌	✅
Proposed Method	✅	✅	✅	❌

How This Technique Works

Gradient-Free and Likelihood-Free Prompt Tuning takes prompt tuning a step further by removing the dependency on gradients and likelihood scores altogether. Instead of using standard backpropagation to update the soft prompt, this technique optimizes the prompt in a gradient-free and likelihood-free manner.

1. Gradient-Free Optimization: Instead of using gradients (which require internal model data), this approach leverages optimization methods, like evolutionary algorithms, to explore the space of soft prompts. Techniques such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are used to iteratively search for effective prompt configurations without needing backpropagation.

2. Likelihood-Free Adaptation: Traditional methods rely on the model’s likelihood scores or logits to adjust the prompt. However, in a black-box scenario, these scores are inaccessible. This method uses only the discrete outputs (for example, the text generated by the model) to guide the optimization process, making it likelihood-free.

3. Uncertainty Quantification: Rather than optimizing a single soft prompt, the method learns a distribution over prompts. This allows for better calibration of the model’s predictions and improves the detection of out-of-distribution (OOD) inputs, as the uncertainty in the prompt can be directly estimated.

Optimization Strategies

Strategy	Description	Benefits
Evolutionary Algorithms (CMA-ES)	Uses an evolutionary optimization method to adjust the prompt without requiring gradients.	Works directly with black-box models and effectively explores the prompt space.
Simulation-Based Inference (ABC-SMC)	Treats the model as a simulator, applying approximate Bayesian computation to optimize the prompt.	Operates without likelihood scores and enables uncertainty estimation.
Monte Carlo Estimation	Uses multiple sampling approaches to estimate the distribution of effective prompts.	Improves model calibration and enhances OOD detection, leading to more reliable predictions.

When to Use This Technique

This gradient-free and likelihood-free approach is particularly valuable in scenarios such as:

• When the model is accessible only through an API with no access to gradients or internal scores.
• In settings where internal model details are protected for privacy or commercial reasons.
• Where reliable confidence estimation is crucial, such as in medical diagnosis or legal decision-making.
• When you have limited labeled data and need an efficient method to adapt the model.
• Where knowing the uncertainty in predictions helps identify inputs that are very different from the training data.

Conclusion

Gradient-Free and Likelihood-Free Prompt Tuning offers a practical and efficient way to adapt black-box language models for specific tasks without requiring access to internal gradients or likelihood scores.

It is a valuable tool for adapting large models in privacy-sensitive and resource-constrained environments.

Footnotes

1. Shen, M., Ghosh, S., Sattigeri, P., Das, S., Bu, Y., & Wornell, G. (2023). Reliable Gradient-free and Likelihood-free Prompt Tuning. https://arxiv.org/abs/2305.00593 ↩