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Discrete prompts are the main method for interacting with Large Language Models (LLMs) due to their interpretability and cross-model compatibility. However, optimizing them for fine-grained tasks such as Aspect-Based Sentiment Analysis (ABSA) remains challenging, particularly due to error propagation from fixed prediction orders. This problem comes from two issues: errors that cascade in the sequence and the need for intensive human involvement in the prompt design. To solve these problems, we present LM-SODP, a Reinforcement Learning (RL) framework that automatically finds a better discrete prompt and decides a better order to make predictions for ABSA. Our method is based on a distilled GPT-2. It improves how the model uses task-specific information and reduces uncertainty by optimizing the prompts. This reduces the output entropy. LM-SODP also independently finds a better execution sequence for the subtasks in ABSA. Experiments on public datasets show that our method leads to stable improvements under different conditions. By using the optimized prompts, LM-SODP can effectively guide LMs with limited computational resources. It also maintains good performance across different domains and opens new avenues for automated prompt token generation.
Bu et al. (Tue,) studied this question.