LigBench：面向基于大语言模型的科研构思生成的统一且人类对齐的基准
文章背景与核心概要
随着大语言模型（LLM）在辅助科学发现（如文献检索和新颖研究构思的提出）方面的应用日益广泛，如何评估这些生成构思的质量仍然是一个重大挑战。当前的评估实践较为零散，缺乏客观标准，且高度依赖直接的 LLM 打分，这限制了其可靠性。
为了克服这些障碍，研究人员推出了 LigBench，这是一个自动化评估基准，旨在对跨不同生成分布的 AI 生成科研构思提供细粒度、可靠且一致的评估。此外，作者还提出了 PAIR-IQ，这是一个专门用于训练成对构思评判模型的专用数据集。实验表明，LigBench 显著提高了与人类专家评估的一致性，而基于 PAIR-IQ 训练的模型则实现了更高的排序准确性和鲁棒性。
Summary / 摘要与总结
随着大语言模型（LLM）的快速发展，科研构思生成受到了越来越多的关注。现有方法使 LLM 能够检索相关文献并为研究领域提出新颖的构思。然而，当前针对构思生成的评估实践仍然碎片化且缺乏客观标准，通常依赖于直接的 LLM 评分，这限制了它们在连贯的生成构思分布中提供统一且可靠评估的能力。
As large language models (LLMs) increasingly assist with scientific discovery—such as retrieving literature and formulating novel research ideas—evaluating the quality of these generated ideas remains a major challenge. Current assessment practices are fragmented, lack objective standards, and heavily rely on direct LLM scoring, which limits reliability.
为了应对这一挑战，我们提出了 LigBench，这是一个自动化评估基准，能够对 AI 科研构思进行细粒度且可靠的评估，并可一致地适用于不同的生成分布。此外，我们引入了 PAIR-IQ，这是一个专为训练成对构思评判模型而定制的数据集，并作为辅助参考以支持更客观的比较评估。大量实验表明，LigBench 实现了稳定且可解释的评估，显著提高了与专家判断的一致性。此外，在 PAIR-IQ 上训练的模型表现出增强的排序准确性和鲁棒性，为可扩展且客观的科研构思评估确立了原则性标准。
To overcome these hurdles, researchers introduce LigBench, an automated evaluation benchmark designed to provide fine-grained, reliable, and consistent assessments of AI-generated research ideas across various generation distributions. Additionally, the authors propose PAIR-IQ, a specialized dataset built for training pairwise idea judgment models. Experiments demonstrate that LigBench significantly improves alignment with human expert evaluations, while models trained on PAIR-IQ achieve superior ranking accuracy and robustness.
Paper Metadata / 论文元数据
- arXiv ID: arXiv:2608.13136 [cs.CL]
- Subject Categories / 学科分类: Computation and Language (
cs.CL), Artificial Intelligence (cs.AI), Databases (cs.DB), Multiagent Systems (cs.MA) - Submission Date / 提交日期: August 13, 2026
- Length / 页数: 17 pages
- DOI: 10.48550/arXiv.2608.13136
Authors / 作者团队
- Chenrun Wang
- Mingxuan Zhu
- Tiancheng Huang
- Wenjie Li
- Yujie Zhang
- Zichen Zhu
- Zhiying Zou
- Kai Yu
- Lu Chen
Abstract / 摘要
With the rapid advancement of large language models (LLMs), research idea generation has attracted increasing attention. Existing approaches enable LLMs to retrieve relevant literature and propose novel ideas for research areas. However, current evaluation practices for idea generation remain fragmented and lack objective standards, often relying on direct LLM scoring, which limits their ability to provide unified and reliable assessments across a coherent distribution of generated ideas. To address this challenge, we propose LigBench, an automated evaluation benchmark that enables fine-grained and reliable evaluation of AI research ideas, consistently applicable across different generation distributions. In addition, we introduce PAIR-IQ, a dataset tailored for training pairwise idea judgment models and serving as an auxiliary reference to support more objective comparative evaluation. Extensive experiments demonstrate that LigBench achieves stable and interpretable evaluations, significantly improving alignment with expert judgments. Furthermore, models trained on PAIR-IQ exhibit enhanced ranking accuracy and robustness, establishing a principled standard for scalable and objective research idea assessment.