InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

TL;DR

InnoEval introduces a knowledge-grounded, multi-perspective framework for scientific idea evaluation, leveraging diverse evidence and expert consensus to emulate human judgment and improve over existing methods.

Contribution

The paper presents InnoEval, a novel deep evaluation framework that combines dynamic knowledge retrieval and multi-disciplinary expert review to enhance scientific idea assessment.

Findings

Outperforms baseline evaluation methods in multiple metrics

Achieves high alignment with human expert judgments

Demonstrates effective multi-dimensional and consensus evaluation

Abstract

The rapid evolution of Large Language Models has catalyzed a surge in scientific idea production, yet this leap has not been accompanied by a matching advance in idea evaluation. The fundamental nature of scientific evaluation needs knowledgeable grounding, collective deliberation, and multi-criteria decision-making. However, existing idea evaluation methods often suffer from narrow knowledge horizons, flattened evaluation dimensions, and the inherent bias in LLM-as-a-Judge. To address these, we regard idea evaluation as a knowledge-grounded, multi-perspective reasoning problem and introduce InnoEval, a deep innovation evaluation framework designed to emulate human-level idea assessment. We apply a heterogeneous deep knowledge search engine that retrieves and grounds dynamic evidence from diverse online sources. We further achieve review consensus with an innovation review board containing reviewers with distinct academic backgrounds, enabling a multi-dimensional decoupled evaluation across multiple metrics. We construct comprehensive datasets derived from authoritative peer-reviewed submissions to benchmark InnoEval. Experiments demonstrate that InnoEval can consistently outperform baselines in point-wise, pair-wise, and group-wise evaluation tasks, exhibiting judgment patterns and consensus highly aligned with human experts.