S truct E val: Deepen and Broaden Large Language Model Assessment via Structured Evaluation (2024)

Abstract: 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 struggle to discern whether a model genuinely possesses the required capabilities or merely memorizes/guesses the answers to specific questions. To this end, this paper proposes 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 evaluations for large language models. 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 a more reliable and consistent conclusion regarding model capabilities. Our framework also sheds light on the design of future principled and trustworthy LLM evaluation protocols.

## introduction evaluation is fundamental for the development of large language models (llms) @xcite @xcite , providing essential measurements, feedback, and insights that facilitate enhancements in helpfulness, reliability and security @xcite . consequently, a variety of large-scale benchmarks are proposed to assess llms' capabilities, such as language understanding @xcite figure 1 : the illustrations for previous single-item assessment and our structured evaluation paradigm. to address the aforementioned challenges, previous research has primarily attempted to manually construct newer, harder, and more diverse benchmarks. for instance, @xcite ; @xcite devised evaluation benchmarks drawing from recent news or articles; @xcite added perturbations into the original datasets to assess model robustness; @xcite ; @xcite collected test instances from human professional examination to increase difficulty and diversity. despite the substantial resource invested, the single-item assessment paradigm of previous benchmarks still struggles with determining whether the evaluated performance can faithfully and fairly reflect the capabilities of models. in this paper, we propose a novel structured evaluation framework named structeval, which can comprehensively, robustly and validly evaluate llms. this is achieved by employing a structured assessment guided by pedagogy theories to evaluate model ability for each test objective across multiple cognitive levels and critical concepts, rather than relying on the correctness of a single test instance. specifically, as illustrated in figure 1b , structeval consists of two modules which deepen and broaden current evaluation respectively. given a seed instance, the first module identifies its underlying test objective, and then generates multiple test instances around this test objective which are aligned with the six cognitive levels outlined in bloom's taxonomy @xcite . meanwhile, the second module extracts the key concepts that must be understood to answer the seed question @xcite , and then develop a series of instances revolving around these concepts based on knowledge graph. unlike single-item assessment, for each test objective, structeval requires llms to demonstrate knowledge across multiple cognitive levels and a thorough comprehension of critical concepts for good performance. in this way, for each test objective, the assessment conclusion is no longer determined by the correctness of a single instance. as a result, it does not depend on confounders introduced by specific instances, such as prompt selection, surface form shortcut, data distribution, etc. therefore, structeval can reduce the impact of biases brought by these confounders, providing more consistent and accurate assessment conclusions for various llms. meanwhile, a model with data contamination can merely memorize specific answers but still lacks corresponding structured knowledge, therefore, structeval can robustly provide stable assessment results even when the training data is contaminated. moreover, due to structeval's capability to automatically generate large-scale and high-quality instances, thereby realizing dynamic evaluation through updating of knowledge sources, it can also prevent benchmarks from rapidly reaching saturation. to demonstrate the effectiveness of our framework, we implement structeval based on 3 widely used benchmarks. the experiments on a variety of llms demonstrate that structeval: 1) enables the automating generation of large-scale benchmarks and completion of structured evaluations, while ensures instance correctness, relevance, and helpfulness. 2) effectively resists the risk of data contamination, providing robust evaluation results even under data contamination settings. 3) significantly enhances the consistency of model rankings across different experiments, offering more precise and stable conclusions from evaluations. 4) substantially outperforms previous augmentation-based strategies such as word perturbation, paraphrasing, back translation, option shuffle, etc. the main contributions of this paper include: ## preliminaries evaluation is the cornerstone for the progress of llms @xcite . unfortunately, there still exist several grand challenges for achieving comprehensive and trustworthy evaluation for llms. for instance, the inability to scale in complexity and diversity at the same pace as the rapid advancements in model capabilities @xcite ; the biases or shortcuts that lead to unfaithful assessments @xcite ; and the lack of reliable metrics for providing trustworthy results @xcite . to this end, previous studies have mainly devoted to improving the diversity, scale, difficulty and timeliness of test instances @xcite , exploring the robustness and trustworthiness vulnerabilities in current evaluations @xcite , and proposing metrics or protocols more suitable for generative lms @xcite . in comparison, this paper aims to propose a structured evaluation framework for llm evaluation. structeval framework is guided by two pedagogy theories which are widely used for educational assessment. bloom's taxonomy theory is a hierarchical model 2 used for classification of educa2 we adopt the cognitive domain list since it is frequently tional learning objectives into six levels, including remember, understand, apply, analysis, evaluate and create @xcite . therefore, to comprehensively evaluate the model's ability across various cognitive levels on test objective, structeval would generate multiple test instances covering six cognitive levels in bloom's taxonomy. concept mapping theory is another well-known tool for student assessment. educators use concept maps to assess the breadth of a student's understanding of a subject, which reveals how well students grasp connections among concepts @xcite . therefore, to assess whether the model genuinely possess the knowledge required for test instance, structeval would develop a series of instances revolving the critical concepts based on knowledge graph. ## structeval framework the overall framework of structeval is illustrated in figure 2 , which consists of two modules. given a seed instance, the first module would evaluate the model's ability on test objective across multi cognitive level. it first identifies the underlying test objective of this instance and then generates multiple relevant instances covering six cognitive levels of bloom's taxonomy. the second module evaluates the model's comprehensive understanding of all critical concepts related to the seed instance. it extracts the essential concepts that must be understood and develops a series of extended questions around these concepts using a knowledge graph. in the following, we would describe the structeval framework in detail. ## implementations and experiments of structeval in this section, we first implement structeval across three widely-used benchmarks. through human evaluation, we demonstrate capability of structeval to automatically construct large-scale benchmarks while ensuring the helpfulness, answerability and correctness of generated instances. then, we demonstrate how structeval could improve the robustness and consistency of llm evaluation from the following perspectives. firstly, structeval requires llms to understand the test objective across multiple cognitive levels and critical concepts. in this case, a contaminated model which merely memorize specific answers may achieve high performance in original benchmark, but cannot gain performance improvements on the structured evaluation since it lacks of corresponding knowledge. therefore, structeval can effectively resist data contamination issues, providing robust evaluation results even when the test data is leaked. secondly, since the evaluation results do not rely on the correctness on single instance, it does not depend on confounders introduced by specific instances, such as prompt selection, surface form shortcut and data distribution. therefore, compared with single-item assessment, structeval can provide a much more robust and consistent evaluation conclusion. ## conclusion this paper proposes a novel evaluation framework for large language models named structeval. through structurally evaluating model's capability for each test objective across multiple cognitive levels and critical concepts, structeval achieves more comprehensive, robust and consistent evaluation for llms. experimental results demonstrate that structeval could effectively resist the risk of data contamination and significantly improve the rank consistency across models. the corresponding benchmarks and leaderboard will be released, which will benefit our understanding of llms' capabilities. structeval is also broadly applicable to various applications. for instance, structeval can function as a customizable benchmark construction framework, capable of automating evaluations for any granularity of assessment objectives, please refer to appendix a for details and experiments. furthermore, our study also sheds light on the design of future principled and trustworthy instance collection and llm evaluation protocols.