Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains challenging due to inherent subjectivity, variability, and scale. Large language models (LLMs) have achieved remarkable success, leading to “LLM-as-a-judge,” where LLMs serve as evaluators for complex tasks. With their ability to process diverse data types and provide scalable assessments, LLMs present a compelling alternative to traditional expert-driven evaluations. However, ensuring the reliability of LLM-as-a-judge systems remains a significant challenge requiring careful design and standardization. This paper provides a comprehensive survey of LLM-as-a-judge, offering a formal definition and detailed classification while addressing the core question of how to build reliable LLM-as-a-judge systems. We explore strategies to enhance reliability, including improving consistency, mitigating biases, and adapting to diverse scenarios. We propose methodologies for evaluating reliability, supported by a novel benchmark. To advance development and deployment, we discuss practical applications, challenges, and future directions. Our contributions span multiple levels: we establish conceptual boundaries, reorganize fragmented literature into a unified framework, and propose a reliability-oriented benchmark. We articulate a forward-looking research agenda, offering theoretical foundations and practical guidance for constructing reliable and trustworthy LLM-as-a-judge systems.

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM’s evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems.

In the context of the global economic downturn caused by COVID-19, e-commerce has become the first choice for people to shop. Many merchants choose to launch promotion activities through some social media platforms. Price discounts can dramatically increase sales volume in social e-commerce due to the interaction of online consumers. It is urgent for e-commerce merchants to learn rules about discount information dissemination in social media, so as to formulate reasonable discount strategies and achieve sustainable business. This paper constructs an evolutionary game model for e-commerce platforms and merchants when they implement promotion strategies through social media, investigates discount information dissemination among consumers under multiple situations by introducing price discount parameters, and further discusses the influence mechanism of discount size and platform reputation on consumers’ purchase behavior. Results show that in low-reputation e-commerce platforms, the price discount is the main motivation to purchase. Consumers’ preference for the high discount is weakened by the increase in platforms’ reputation. Discounts should be set according to the different reputations. Businesses in a high-reputation environment are relatively more profitable. E-commerce businesses should work together, apply reasonable pricing, and improve their quality to create a green and healthy shopping environment, in order to get benefits and sustainable development.

With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to fill in missing facts for a long time. However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity). In practice, n-ary relational facts are also ubiquitous. When encountering such facts, existing studies usually decompose them into triples by introducing a multitude of auxiliary virtual entities and additional triples. These conversions result in the complexity of carrying out link prediction on n-ary relational data. It has even proven that they may cause loss of structure information. To overcome these problems, in this paper, we represent each n-ary relational fact as a set of its role and role-value pairs. We then propose a method called NaLP to conduct link prediction on n-ary relational data, which explicitly models the relatedness of all the role and role-value pairs in an n-ary relational fact. We further extend NaLP by introducing type constraints of roles and role-values without any external type-specific supervision, and proposing a more reasonable negative sampling mechanism. Experimental results validate the effectiveness and merits of the proposed methods.

Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Zixuan Li, Jian Guo, Huawei Shen, Yuanzhuo Wang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.