O pen E val: Benchmarking C hinese LLM s across Capability, Alignment and Safety (2024)

Abstract: The rapid development of Chinese large language models (LLMs) poses big challenges for efficient LLM evaluation. While current initiatives have introduced new benchmarks or evaluation platforms for assessing Chinese LLMs, many of these focus primarily on capabilities, usually overlooking potential alignment and safety issues. To address this gap, we introduce OpenEval, an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety. For capability assessment, we include 12 benchmark datasets to evaluate Chinese LLMs from 4 sub-dimensions: NLP tasks, disciplinary knowledge, commonsense reasoning and mathematical reasoning. For alignment assessment, OpenEval contains 7 datasets that examines the bias, offensiveness and illegalness in the outputs yielded by Chinese LLMs. To evaluate safety, especially anticipated risks (e.g., power-seeking, self-awareness) of advanced LLMs, we include 6 datasets. In addition to these benchmarks, we have implemented a phased public evaluation and benchmark update strategy to ensure that OpenEval is in line with the development of Chinese LLMs or even able to provide cutting-edge benchmark datasets to guide the development of Chinese LLMs. In our first public evaluation, we have tested a range of Chinese LLMs, spanning from 7B to 72B parameters, including both open-source and proprietary models. Evaluation results indicate that while Chinese LLMs have shown impressive performance in certain tasks, more attention should be directed towards broader aspects such as commonsense reasoning, alignment, and safety.

## introduction large language models have demonstrated remarkable capabilities across multiple natural language processing (nlp) tasks @xcite and real-world applications. for instance, chatgpt foot_0 has captivated users with its human-like interaction and instruction-following skills, while @xcite has advanced llms to a new stage, showcasing superior performance compared to chatgpt. meanwhile, a rapid development of both pre-trained chinese llms @xcite @xcite and supervised fine-tuning/reinforcement learning from human feedback (sft/rlhf) chinese llms @xcite has also been witnessed, creating a formidable array of models. 3 however, traditional nlp benchmarks @xcite may not be suitable for evaluating chinese llms due to their limitations (e.g., being tailored for benchmarking a specific task rather than generality). in order to evaluate to what extent chinese llms capture general and domain-specific knowledge, several chinese benchmarks @xcite @xcite have been proposed, which usually directly collect questions from human examinations across different grades. with the evolving capabilities of chinese llms, new benchmarks have been explored to assess capability aspects such as coding @xcite , role-playing @xcite , mathematical reasoning @xcite , etc. in addition to knowledge and capability, value alignment is also crucial for llms, which aligns the outputs yielded by llms to human preferences 1 190 in multiple aspects of human values (e.g., harmless, helpfulness, morality) @xcite via various sft/rlhf methods @xcite @xcite . in corresponding to the assessment of chinese llms alignment, several datasets have been curated, e.g, datasets for evaluating bias @xcite , chinese profanity @xcite , online sexism @xcite . recently, llm safety (weidinger et al., 2021) has been emerging as a critical concern, especially for advanced llms, owing to their unpredictable behaviors. unfortunately, current safety evaluation efforts for chinese llms usually concentrate on established social and ethical risks (e.g., generating content violating social norms) @xcite , overlooking the potential catastrophic consequences @xcite of llm behaviors such as decision-making @xcite and powerseeking @xcite @xcite , as evidenced in existing studies. chinese llms evaluation platforms like flageval (contributors, 2023a), cleva @xcite , and opencompass @xcite do not include such safety evaluation. in order to bridge these gaps, providing multidimensional evaluations for chinese llms, which cover capability, alignment and safety with diverse benchmarks, becomes a desideratum. we hence introduce openeval, a comprehensive, userfriendly, scalable, and transparent platform for assessing open-source and proprietary chinese llms. openeval focuses not only on various capabilities like knowledge capturing and reasoning, but also on alignment and potential risks of advanced llms. users can easily access their llms through openeval. meanwhile, the platform is adaptable, allowing for the replacement of existing benchmarks with new tasks to maintain an updated and unbiased testing environment. it also offers leaderboards and evaluation reports, providing users with insights into the llm's performance and detailed suggestions on strengths and weaknesses. following the evaluation taxonomy proposed by @xcite , we have organized chinese datasets in openeval by capability, alignment, and safety. for capability, we further divide it into four sub-dimensions: nlp tasks, disciplinary knowledge, commonsense reasoning, and mathematical reasoning. the alignment dimension consists of datasets evaluating bias, toxicity and other value alignment aspects in llms. for safety, we have selected datasets to monitor undesirable behaviors in chinese llms, such as power-seeking @xcite , situational awareness (shevlane et al., 2023), self-improving @xcite , etc. to facilitate the use of these benchmark datasets for llm evaluation, unique prompts have been created for each task to leverage llms' ability to follow instructions, with specific metrics tailored to each task. in our first public evaluation with openeval, we have assessed 9 open-source chinese llms ranging from 6b to 72b, and 5 proprietary chinese llms developed by big companies. based on our evaluation results, we find several significant differences between open-source and proprietary chinese llms. generally, proprietary chinese llms demonstrate a clear advantage in disciplinary knowledge and mathematical reasoning capabilities. however, they lag behind open-source llms in terms of alignment and safety. additionally, both proprietary and open-source chinese llms display inadequate performance in commonsense reasoning. the main contributions of our work are as follows. ## related work llm evaluations are rapidly evolving alongside the advancement of llms. while traditional nlp benchmarks @xcite @xcite @xcite are typically tailored to a single task and require model training on their specific training data, modern practices of assessing llms usually have them perform diverse tasks under the few-or zero-shot setting. consequently, current benchmarks @xcite seek to evaluate llms across various domains, from knowledge @xcite , reasoning @xcite , alignment @xcite to safety @xcite . take the knowledge evaluation as an example. inspired by mmlu @xcite , a variety of knowledge-oriented chinese benchmarks, e.g., c-eval @xcite , @xcite , and cmmlu @xcite , have been recently developed to evaluate the knowledge capturing and understanding of chinese llms over a wide range of subjects within the chinese education system. in addition to these benchmarks that aims at evaluating a specific aspect of llms, efforts have been also explored to build chinese llm evaluation platforms that attempt to comprehensively evaluate llms with a suite of benchmarks. flageval (contributors, 2023a) is a multilingual and multimodal evaluation platform that includes benchmarks for nlp and computer vision (cv) tasks in chinese and english. opencompass (contributors, 2023b) is an evaluation platform designed for chinese llms. it presents a varied range of benchmarks covering reading comprehension, question answering, reasoning, and more, enabling a thorough evaluation of llm capabilities in chinese nlp tasks. cleva @xcite ) is a recent platform introduced for comprehensive evaluation of chinese llms. like opencompass, its goal is to offer a broad suite of benchmarks for assessing chinese llms across various language understanding and generation tasks. in contrast to these efforts, openeval not only evaluates the capability and alignment of chinese llms, but also assesses the safety issue associated with advanced llms, leading to a more comprehensive evaluation. ## data pre-processing and post-processing llms have shown impressive performance across multiple tasks when provided with instructions. as a result, we have included a specific prompt for each task based on the corresponding task description. examples of prompts are shown in appendix b. in the current version of openeval, we collect 25 datasets and further split them into 53 tasks. ultimately, around 300k questions have been reformulated in a unified form using appropriate prompts for the zero-shot evaluation setting. users can also modify the prompts by themselves, as different llms use different prompts that are defined dur-ing their fine-tuning stage. notably, the evaluation dimension that consists of the largest number of datasets and tasks is capability. conversely, safety is the evaluation dimension with the smallest number of datasets, indicating a lack of available datasets for assessing llms' safety. llms may not strictly adhere to user instructions. for instance, in a multiple-choice qa task, even being instructed to only predict the final option without additional explanations, some llms may still generate surplus content that contradicts the measurement metric, such as accuracy. hence, we offer task-specific answer selection methods in openeval based on their metric descriptions. for example, in a multiple-choice qa task, we choose the first uppercase letter from the llm output as the final answer. ## evaluation taxonomy inspired by @xcite , we design an evaluation taxonomy with three major dimensions for openeval, which are capability, alignment, and safety, as illustrated in figure 1 . this indicates that openeval not only focuses on llms' proficiency in traditional nlp tasks but also measures to what extent llms align with human values and tend towards undesirable behaviors. in essence, we envision openeval having the potential to monitor advanced llms along their evolvement. ## evaluation strategy to maintain the efficiency and transparency of openeval as well as mitigate potential data contamination, we take a variety of evaluation strategies. ## experiments we have organized the first public evaluation campaign with openeval for chinese llms. this section presents main results for both evaluated opensource and proprietary chinese llms and in-depth analyses on the results. ## conclusion in this paper, we have presented openeval, a comprehensive evaluation platform for chinese llms. we not only assess llms' capabilities but also take alignment and safety evaluation into account, paving the way for monitoring advanced llms in the future. openeval includes 53 tasks with ∼ 300k questions. additionally, we employ a dynamic evaluation strategy to ensure that openeval stays effective by replacing outdated or contaminated benchmarks with new ones. we plan to conduct the second round of evaluations to pinpoint the strengths and weaknesses of chinese llms in a broader way than the first evaluation. this will involve the development of new benchmarks and the organization of shared tasks aiming at general evaluations, specialized llms evaluations and evaluations tailored for specific llm application scenarios.