OBELLA: Open the Book for Evaluating Long-Form Large Language Model Answers in Open-Domain Question Answering

Abstract

Reliable factuality evaluation is critical for the iterative development of open-domain question answering (ODQA) systems, especially given the rise of large language models (LLMs) and their propensity for hallucination. However, state-of-the-art (SOTA) automatic metrics, which are mostly supervised, remain notably less reliable than humans. In this paper, we find two key challenges behind this gap: (1) length distribution mismatch between lengthy LLM answers and shorter training answers used by current metrics; and (2) reference incompleteness, where current metrics often misjudge valid system answers absent from given references-a challenge worsened by the diversity of LLM outputs. To address these issues, we present a new ODQA factuality evaluation dataset called OBELLA (Open-Book Evaluation for Long-form LLM Answers). OBELLA narrows the length distribution mismatch by significantly increasing the candidate answer length to align with LLM outputs. Moreover, it introduces a neutral class for plausible yet under-supported candidate answers to differentiate reference incompleteness from outright incorrectness, thus enabling flexible reevaluation by consulting external knowledge for more references. Based on OBELLA, we propose a novel metric named OBELLAM (OBELLA Metric). OBELLAM integrates a cross-attention mechanism to enhance long-form candidate answer representations and employs a dynamic closed-open book evaluation strategy to tackle reference incompleteness. Our OBELLAM sets a new SOTA in aligning with human judgments across two ODQA evaluation benchmarks, marking a promising step toward more robust ODQA factuality evaluation.