Entropy-guided test-time augmentation based on large language model for secret point recognition
In the domain of confidentiality management, secret point recognition stands as a precise and efficient technical solution. However, it faces significant hurdles in machine learning-based implementations: stringent confidentiality constraints on confidential data and restricted data accessibility often lead to insufficient labeled samples, which severely compromise recognition performance. To address this challenge, this paper proposes a secret point recognition algorithm that leverages test-time augmentation (TTA) based on a large language model (LLM) and entropy increase judgment. The algorithm operates in two key phases. During the training phase, a pre-trained language model is fine-tuned using a small volume of labeled confidential data to establish a foundational classification capability. During inference, an LLM is employed to paraphrase the input text, generating multiple variants that retain the core semantics while adopting diverse linguistic styles. To ensure increased diversity of the paraphrased text, an entropy increase judgment is introduced. These paraphrased texts serve as augmented data and are then fed into the fine-tuned classification model to produce preliminary predictions. Finally, a dedicated aggregation strategy consolidates these preliminary results to yield the final prediction for the original input text. The results of experiments using simulated confidential data indicate that the proposed algorithm can effectively improve recognition accuracy, demonstrating its superiority in low-sample scenarios.