Ji Wu

Yuxuan Zhou, Xien Liu, Chen Ning et al.

Large language models (LLMs) have excelled across domains, also delivering notable performance on the medical evaluation benchmarks, such as MedQA. However, there still exists a significant gap between the reported performance and the practical effectiveness in real-world medical scenarios. In this paper, we aim to explore the causes of this gap by employing a multifaceted examination schema to systematically probe the actual mastery of medical knowledge by current LLMs. Specifically, we develop a novel evaluation framework MultifacetEval to examine the degree and coverage of LLMs in encoding and mastering medical knowledge at multiple facets (comparison, rectification, discrimination, and verification) concurrently. Based on the MultifacetEval framework, we construct two multifaceted evaluation datasets: MultiDiseK (by producing questions from a clinical disease knowledge base) and MultiMedQA (by rephrasing each question from a medical benchmark MedQA into multifaceted questions). The experimental results on these multifaceted datasets demonstrate that the extent of current LLMs in mastering medical knowledge is far below their performance on existing medical benchmarks, suggesting that they lack depth, precision, and comprehensiveness in mastering medical knowledge. Consequently, current LLMs are not yet ready for application in real-world medical tasks. The codes and datasets are available at https://github.com/THUMLP/MultifacetEval.

Xiao-Lei Zhang, Ji Wu

Representation learning and unsupervised learning are two central topics of machine learning and signal processing. Deep learning is one of the most effective unsupervised representation learning approach. The main contributions of this paper to the topics are as follows. (i) We propose to view the representative deep learning approaches as special cases of the knowledge reuse framework of clustering ensemble. (ii) We propose to view sparse coding when used as a feature encoder as the consensus function of clustering ensemble, and view dictionary learning as the training process of the base clusterings of clustering ensemble. (ii) Based on the above two views, we propose a very simple deep learning algorithm, named deep random model ensemble (DRME). It is a stack of random model ensembles. Each random model ensemble is a special k-means ensemble that discards the expectation-maximization optimization of each base k-means but only preserves the default initialization method of the base k-means. (iv) We propose to select the most powerful representation among the layers by applying DRME to clustering where the single-linkage is used as the clustering algorithm. Moreover, the DRME based clustering can also detect the number of the natural clusters accurately. Extensive experimental comparisons with 5 representation learning methods on 19 benchmark data sets demonstrate the effectiveness of DRME.