Evaluating C hinese Large Language Models on Discipline Knowledge Acquisition via Memorization and Robustness Assessment (2024)

Abstract: Chinese LLMs demonstrate impressive performance on NLP tasks, particularly on discipline knowledge benchmarks, with some results approaching those of GPT-4. Previous research has viewed these advancements as potential outcomes of data contamination or leakage, prompting efforts to create new detection methods and address evaluation issues in LLM benchmarks. However, there has been a lack of comprehensive assessment of the evolution of Chinese LLMs. To address this gap, this paper offers a thorough investigation of Chinese LLMs on discipline knowledge evaluation, delving into the advancements of various LLMs, including a group of related models and others. Specifically, we have conducted six assessments ranging from knowledge memorization to comprehension for robustness, encompassing tasks like predicting incomplete questions and options, identifying behaviors by the contaminational fine-tuning, and answering rephrased questions. Experimental findings indicate a positive correlation between the release time of LLMs and their memorization capabilities, but they struggle with variations in original question-options pairs. Additionally, our findings suggest that question descriptions have a more significant impact on LLMs’ performance.

## introduction large language models @xcite have demonstrated remarkable capabilities through alignment technologies @xcite such as supervised fine-tuning (sft) @xcite and reinforcement learning from human feedback (rlhf) @xcite . while the primary language domain of llms is english, the emergence of chinese llms (du et al., 2022; zeng et al., 2023a; bai et al., 2023; team, 2023; ‡ corresponding author. @xcite is creating another large community. a key question arises on how to effectively evaluate these advanced chinese llms. although there are various datasets for benchmarking chinese llms, covering areas such as instructionfollowing @xcite , bias detection @xcite , and code generation @xcite , the widely accepted approach involves gathering multiple-choice questions from human exams to serve as a benchmark for assessing chinese llms across a range of subjects, thereby establishing a standardized testing framework for chinese llms. several chinese llms have made significant progress on discipline knowledge benchmarks @xcite @xcite . current results obtained in these benchmarks indicate that the performance of certain chinese llms is approaching that of @xcite . however, these benchmarks currently rely solely on accuracy as the primary evaluation metric, offering limited insights into assessment results. moreover, discipline knowledge benchmarks usually collect questions from publicly available online sources, which could potentially overlap with llm pre-training data. additionally, once benchmarks are released, developers might unconsciously use them as training data for their llms. this introduces challenges related to data contamination and leakage, leading to misleading progress assessments. existing efforts aim to detect data contamination through various methods @xcite @xcite . for instance, @xcite introduce a technique for identifying data contamination without relying on references. however, it has been observed by @xcite that existing methods struggle to detect altered questions, prompting them to utilize llms for question rewriting to enhance detection capabilities. despite these advancements, a com-prehensive analysis for chinese llms on this issue is still lacking. in this paper, we conduct a thorough investigation into the advancements of chinese llms in the field of discipline knowledge based on the m3ke benchmark @xcite . our analysis spans two key dimensions: memorization and robustness. these dimensions offer a multi-faceted approach to evaluating chinese llms beyond mere accuracy. for the memorization dimension, we have employed three sub-dimensions to assess the models. initially, we evaluate the ability of chinese llms to memorize questions and options from the m3ke dataset under various conditions like zero-shot and few-shot scenarios. subsequently, we fine-tune an llm on m3ke using different proportions to compare genuine contamination with instances where contamination is unclear. lastly, we evaluate six llms by removing the questions and considering only the options as input based on a hypothesis that llms are likely to predict correct option without the question if they have memorized those test data. in the robustness dimension, we also have utilized three sub-methods, including shuffling option orders, question rewriting by @xcite , and a combination of rewritten questions and shuffled options. this approach allows for a comprehensive comparison among chinese llms whether those llms response to changes in sample description from the benchmark. our study involves two sets of chinese llms for a more thorough investigation. the first group comprises chatglm models, such as chatglm1-6b, foot_0 chatglm2-6b, foot_1 and chatglm3-6b, foot_2 which are based on the same pre-trained llm @xcite of identical size but varying versions. the second group consists of llms @xcite @xcite of similar sizes but differing pre-trained models. by selecting these distinct groups, we aim to conduct a precise analysis across different versions and pre-trained models. various experiments indicate that llms possess a wealth of disciplinary knowledge and can handle questions, yet they remain sensitive to variations like different option orders and altered question descriptions, particularly the latter. our main contributions in the paper are as follows: task input output 1 question a:text, b:text, c:text, d:text 2 question + a: text, b:text, c:text, d:text 3 question + a:text + b:text c:text, d:text 4 demonstrations + question a:text, b:text, c:text, d:text in this paper, we focus on benchmarks with disciplinary knowledge for two primary reasons. firstly, these benchmarks cover a variety of subjects, leading to a thorough assessment. secondly, benchmarks of this nature are commonly used as the standard evaluation in llm publications. therefore, we have chosen m3ke @xcite as our testbed due to its wide coverage of questions and subjects. ## methodology concerning memorization, there are three further sub-dimensions. initially, we employ a pre-training task to investigate the memorization capabilities of chinese llms. subsequently, we compare directly fine-tuning the earliest version of llm released before m3ke, utilized in this paper, with other llms. finally, we eliminate each question from the input, providing only four options to the llms, to assess whether they can offer correct answers without the question. for robustness, we randomize the order of options and rewrite questions separately, yielding a different perspective. ## experiments we conducted extensive experiments to re-evaluate chinese llms from the perspectives of memorization and robustness. ## conclusion in this paper, we have conducted a series of experiments to explore current progresses of chinese llms on the discipline knowledge benchmark. we evaluated six chinese sft/rlhf llms belong to different groups to whether the new generation llm memories more knowledge than the previous one, and the llm taking more knowledge is able to handle those questions with different de-scriptions. experiment results suggest although the newer llm memorizes more knowledge, it still struggles with variations on the question, especially the description of question has more impact on llms. given that data contamination may pervade across different dimensions of llm evaluation, we are keen to encourage the community further investigate current performance on public benchmarks.