Evaluation is the baton for the development of large language models. Current evaluations typically employ a single-item assessment paradigm for each atomic test objective, which struggles to discern whether a model genuinely possesses the required capabilities or merely memorizes/guesses the answers to specific questions. To this end, we propose a novel evaluation framework referred to as StructEval. Starting from an atomic test objective, StructEval deepens and broadens the evaluation by conducting a structured assessment across multiple cognitive levels and critical concepts, and therefore offers a comprehensive, robust and consistent evaluation for LLMs. Experiments on three widely-used benchmarks demonstrate that StructEval serves as a reliable tool for resisting the risk of data contamination and reducing the interference of potential biases, thereby providing more reliable and consistent conclusions regarding model capabilities. Our framework also sheds light on the design of future principled and trustworthy LLM evaluation protocols.

Beyond these optimizations, meeting the system To enhance user experience and conversion efficiency, the online performance requirements presents a significant challenge. Contrasting search system is employed with a cascading architecture, mainly with existing industry works, we propose a novel method: a including recall and ranking. The ranking stage as the downstream Generalizable and RAnk-ConsistEnt Pre-Ranking Model (GRACE), component directly influences the efficiency of item sorting. Several which achieves: 1) Ranking consistency by introducing multiple superior ranking models have been identified in industrial research, binary classification tasks that predict whether a product is within such as MMoE [4], PLE [12], ESMM [5], DeepFM [1], DIN [18], the top-k results as estimated by the ranking model, which facilitates MIMN [8], SDIM [16], and SIM [12], with a focus on feature engineering, the addition of learning objectives on common point-wise behavioral sequence modeling, and objective function ranking models; 2) Generalizability through contrastive learning optimization. However, as the scale of products within the search of representation for all products by pre-training on a subset of system grows, there is an increasing demand for managing the ranking product embeddings; 3) Ease of implementation in feature time complexity of the sorting module.

In recent times, deep neural networks achieved outstanding predictive performance on various classification and pattern recognition tasks. However, many real-world prediction problems have ordinal response variables, and this ordering information is ignored by conventional classification losses such as the multi-category cross-entropy. Ordinal regression methods for deep neural networks address this. One such method is the CORAL method, which is based on an earlier binary label extension framework and achieves rank consistency among its output layer tasks by imposing a weight-sharing constraint. However, while earlier experiments showed that CORAL's rank consistency is beneficial for performance, the weight-sharing constraint could severely restrict the expressiveness of a deep neural network. In this paper, we propose an alternative method for rank-consistent ordinal regression that does not require a weight-sharing constraint in a neural network's fully connected output layer. We achieve this rank consistency by a novel training scheme using conditional training sets to obtain the unconditional rank probabilities through applying the chain rule for conditional probability distributions. Experiments on various datasets demonstrate the efficacy of the proposed method to utilize the ordinal target information, and the absence of the weight-sharing restriction improves the performance substantially compared to the CORAL reference approach.