评估大型语言模型：一项全面综述
Evaluating Large Language Models: A Comprehensive Survey
AI总结：
本综述全面回顾了大型语言模型（LLMs）的评估方法，将其分为知识与能力、对齐和安全三大类，并探讨了专业领域评估与综合评估平台构建，旨在推动LLMs负责任发展。
摘要：
大型语言模型（LLMs）在广泛的任务中展现了卓越的能力。它们引起了广泛关注，并被部署于众多下游应用中。然而，就像一把双刃剑，LLMs也带来了潜在风险。它们可能会遭遇私人数据泄露，或产生不当、有害或误导性的内容。此外，LLMs的快速进展引发了人们对在缺乏充分保障措施的情况下可能出现超级智能系统的担忧。为了有效利用LLMs的能力并确保其安全有益的发展，对LLMs进行严格和全面的评估至关重要。本综述旨在提供LLMs评估的全景视角。我们将LLMs的评估分为三大类：知识与能力评估、对齐评估和安全评估。除了对这三个方面的评估方法和基准进行全面回顾外，我们还整理了与LLMs在专业领域性能相关的评估摘要，并讨论了涵盖LLMs在能力、对齐、安全性和适用性方面评估的综合评估平台的构建。我们希望这一全面概述能激发对LLMs评估的进一步研究兴趣，最终目标是使评估成为指导LLMs负责任发展的基石。我们设想这将引导其演化朝着最大化社会效益同时最小化潜在风险的方向发展。相关论文的精选列表已在 https://github.com/tjunlp-lab/Awesome-LLMs-Evaluation-Papers 公开提供。
英文摘要：
Large language models (LLMs) have demonstrated remarkable capabilities across a broad spectrum of tasks. They have attracted significant attention and been deployed in numerous downstream applications. Nevertheless, akin to a double-edged sword, LLMs also present potential risks. They could suffer from private data leaks or yield inappropriate, harmful, or misleading content. Additionally, the rapid progress of LLMs raises concerns about the potential emergence of superintelligent systems without adequate safeguards. To effectively capitalize on LLM capacities as well as ensure their safe and beneficial development, it is critical to conduct a rigorous and comprehensive evaluation of LLMs. This survey endeavors to offer a panoramic perspective on the evaluation of LLMs. We categorize the evaluation of LLMs into three major groups: knowledge and capability evaluation, alignment evaluation and safety evaluation. In addition to the comprehensive review on the evaluation methodologies and benchmarks on these three aspects, we collate a compendium of evaluations pertaining to LLMs' performance in specialized domains, and discuss the construction of comprehensive evaluation platforms that cover LLM evaluations on capabilities, alignment, safety, and applicability. We hope that this comprehensive overview will stimulate further research interests in the evaluation of LLMs, with the ultimate goal of making evaluation serve as a cornerstone in guiding the responsible development of LLMs. We envision that this will channel their evolution into a direction that maximizes societal benefit while minimizing potential risks. A curated list of related papers has been publicly available at https://github.com/tjunlp-lab/Awesome-LLMs-Evaluation-Papers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
