생성 평가 일관성을 활용한 대형 언어 모델 정렬 벤치마크

On Evaluating LLM Alignment by Evaluating LLMs as Judges Yixin Liu1 Pengfei Liu2 Arman Cohan1 1Yale University 2Shanghai Jiao Tong University {yixin.liu, arman.cohan}@yale.edu Abstract Alignment with human preferences is an important evaluation aspect of LLMs, re- quiring them to be helpful, honest, safe, and to precisely follow human instructions. Evaluating large language models’ (LLMs) alignment typically involves directly assessing their open-ended responses, requiring human annotators or strong LLM judges. Conversely, LLMs themselves have also been extensively evaluated as judges for assessing alignment. In this work, we examine the relationship between LLMs’ generation and evaluation capabilities in aligning with human preferences. To this end, we first conduct a comprehensive analysis of the generation-evaluation consistency (GE-consistency) among various LLMs, revealing a strong correla- tion between their generation and evaluation capabilities when evaluated by a strong LLM preference oracle. Utilizing this finding, we propose a benchmarking paradigm that measures LLM alignment with human preferences without directly evaluating their generated outputs, instead assessing LLMs in their role as evalua- tors. Our evaluation shows that our proposed benchmark, ALIGNEVAL, matches or surpasses widely used automatic LLM evaluation benchmarks, such as AlpacaEval and Arena-Hard, in capturing human preferences when ranking LLMs. Our study offers valuable insights into the connection between LLMs’ generation and eval- uation capabilities, and introduces a benchmark that assesses alignment without directly evaluating model outputs.1 1 Introduction Alignment with human preferences is a key property of LLMs, requiring them to accurately follow user instructions, generate responses that meet user needs, and reflect human values [29, 4]. Evaluating LLM alignment2 typically involves human evaluations of model outputs in response to various user queries, since it requires assessing LLMs’ capabilities in various open-ended tasks. However, such large-scale and reliable human evaluations are often complex, expensive, and time-consuming [46]. To scale this process, the widely used ChatBot Arena benchmark [5] relies on crowd-sourced annotations, where each instance consists of a pairwise comparison between two model outputs for a given instruction. To reduce reliance on the expensive process of human evaluation, automatic alignment benchmarks have been proposed [45, 22, 21, 24], where LLMs as judges are used in place of human annotators, enabling faster evaluation while maintaining a reasonably high level of agreement with human preferences. Consequently, this LLMs-as-Judges paradigm has been commonly used for LLM evaluation in alignment and other open-ended tasks. Understanding and evaluating how reliable LLMs are as judges has also become an important topic [43, 19, 25]. As discussed earlier, LLMs-as-Judges is an important paradigm for LLM evaluation. 1ALIGNEVAL is available at https://github.com/yale-nlp/AlignEval. 2In this work, we use “LLM alignment” to refer to LLMs’ general capabilities in following human instructions and providing helpful, high-quality responses, which goes beyond safety or harmlessness alignment. 39th Conference on Neural Information Processing Systems (NeurIPS 2025). arXiv:2511.20604v1 [cs.CL] 25 Nov 2025 Figure 1: Illustration of Generation-Evaluation Consistency (GE-consistency), where LLMs’ genera- tion and evaluation capability rankings are compared using a preference oracle. Furthermore, LLMs can also be used as judges in model training with preference optimization algorithms in place of fine-tuned reward models [35]. The evaluations of LLMs as judges are typically conducted by comparing the predictions of LLMs against human annotations for the same evaluation tasks. Recent studies [47, 19] have shown that frontier LLMs are strong judges for alignment evaluation. In particular, they can serve as effective out-of-the-box generative reward models [26], achieving performance competitive with fine-tuned discriminative reward models. We argue that it is important to study the connection between these two related capabilities of LLMs: generating responses that align with human preferences and evaluating whether responses are aligned with human preferences. Understanding this connection has significant implications for both model evaluation, by revealing the (in)consistency between these capabilities [41, 23], and model training, particularly in exploring the feasibility of self-improvement where an LLM’s training is supervised by its own judgments, which inherently requires a model to accurately assess its own outputs [32, 42]. Along this direction, prior work has examined the (in)consistency of individual LLMs when acting as both generator and validator [32, 42]. However, a comprehensive study is still lacking on whether the performance rankings among various LLMs are consistent between the gen

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