Actively Learn from LLM s with Uncertainty Propagation for Generalized Category Discovery (2024)

Abstract: Generalized category discovery faces a key issue: the lack of supervision for new and unseen data categories. Traditional methods typically combine supervised pretraining with self-supervised learning to create models, and then employ clustering for category identification. However, these approaches tend to become overly tailored to known categories, failing to fully resolve the core issue. Hence, we propose to integrate the feedback from LLMs into an active learning paradigm. Specifically, our method innovatively employs uncertainty propagation to select data samples from high-uncertainty regions, which are then labeled using LLMs through a comparison-based prompting scheme. This not only eases the labeling task but also enhances accuracy in identifying new categories. Additionally, a soft feedback propagation mechanism is introduced to minimize the spread of inaccurate feedback. Experiments on various datasets demonstrate our framework’s efficacy and generalizability, significantly improving baseline models at a nominal average cost.

## introduction generalized category discovery (gcd) is a crucial task in open-world computing @xcite , where the goal is to automate the classification of partially labeled data. it uniquely challenges systems to not only recognize predefined categories but also to discover entirely new categories from a mix of labeled and unlabeled data @xcite . this task mirrors the dynamic and evolving nature of real-world data, where new categories frequently emerge, necessitating models that can adapt and learn continually. in traditional gcd methods, the initial step often involves supervised pretraining on a labeled dataset to establish a foundational understanding of known categories @xcite . this is followed by self-supervised learning on unlabeled data or even contrastive learning, allowing the model to extract and learn patterns without explicit category labels @xcite . the final stage typically employs clustering techniques, like k-means @xcite , to group similar data points, aiming to identify categories. however, this sequential process tends to imprint a bias towards the initially learned, known categories, thus limiting the model's ability to generalize to new, unseen categories @xcite . such overfitting to familiar data restricts the scope of gcd, preventing it from fully embracing the open-world setting it is intended for. recently, large language models (llms) such as @xcite , palm @xcite , and llama @xcite have shown extraordinary versatility across a broad range of nlp tasks, providing good quality super-vision signals for summarization @xcite , clustering @xcite , etc. their ability to understand and generate nuanced language patterns makes them promising for supplementing the supervision of new categories in gcd. however, the direct application of llms in gcd, which typically involves processing and clustering thousands of samples, raises substantial challenges. the intensive computational demands of llms could lead to issues with data privacy, high latency, and increased costs, which are particularly problematic in large-scale gcd scenarios. to circumvent the above challenges, integrating llms into an active learning framework presents a practical and efficient solution. this approach entails selectively using llms to provide supervision signals, especially in cases where the data is most uncertain or the categories are novel. however, this integration brings forth new challenges: optimizing the use of llms to ensure cost and time efficiency, and critically, ensuring the reliability of the feedback provided by llms. effective strategies are needed to mitigate the risk of propagating incorrect feedback from llms. addressing these challenges, we propose a novel framework for gcd to actively learn from llms with uncertainty propagation, termed as alup. as shown in figure 1 , we begin by employing an uncertainty propagation strategy, which systematically identifies data samples in regions of high uncertainty -these are the areas where the model is least confident and, therefore, where llm input could be most beneficial. the selected samples are then labeled using llms through a sophisticated comparison-based prompting technique. this method leverages the comparative strength of llms, making it easier for them to provide accurate feedback, especially for new and complex categories. to further enhance our approach, we incorporate a soft label propagation mechanism. this mechanism carefully extends the llms-generated feedback to similar, neighboring samples, effectively amplifying the value of each llm query while minimizing the risk of propagating errors. rigorous testing on diverse datasets has shown that our method not only significantly improves upon existing baseline models but also does so with a nominal increase in cost, offering a scalable, efficient, and effective solution for the intricate problem of gcd. the main contributions of this work can be summarized as follows: ## conclusion in summary, our alup framework innovatively integrates large language models with uncertainty propagation in generalized category discovery, marking a significant leap in the field. by employing comparison-based llm prompting and a novel soft feedback propagation mechanism, alup adeptly identifies and categorizes new data with enhanced accuracy and efficiency. this approach not only surpasses traditional gcd methods but also minimizes the risk of error propagation, a critical advancement in handling real-world, dynamic datasets with llms. future endeavors will focus on refining llm integration, extending our methods to multi-modal data, and enhancing scalability and data privacy measures, furthering alup's potential in diverse and evolving openworld computing.