# LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs Source: [LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:22:19.696Z Authors: Sihui Yang, Keping Bi, Wanqing Cui, Jiafeng Guo, Xueqi Cheng Published: 2024-09-23T06:42:21.000Z ## Abstract Non-Factoid (NF) Question Answering (QA) is challenging to evaluate due to diverse potential answers and no objective criterion. The commonly used automatic evaluation metrics like ROUGE or BERTScore cannot accurately measure semantic similarities or answers from different perspectives. Recently, Large Language Models (LLMs) have been resorted to for NFQA evaluation due to their compelling performance on various NLP tasks. Common approaches include pointwise scoring of each candidate answer and pairwise comparisons between answers. Inspired by the evolution from pointwise to pairwise to listwise in learning-to-rank methods, we propose a novel listwise NFQA evaluation approach, that utilizes LLMs to rank candidate answers in a list of reference answers sorted by descending quality. Moreover, for NF questions that do not have multi-grade or any golden answers, we leverage LLMs to generate the reference answer list of various quality to facilitate the listwise evaluation. Extensive experimental results on three NFQA datasets, i.e., ANTIQUE, the TREC-DL-NF, and WebGLM show that our method has significantly higher correlations with human annotations compared to automatic scores and common pointwise and pairwise approaches. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Open-ended Question Answering on TREC-DL-NF (S5)](): Open-ended Question Answering; TREC-DL-NF (S5); Kendall's Tau (K): 65.58. - [Open-ended Question Answering on ANTIQUE (S5)](): Open-ended Question Answering; ANTIQUE (S5); Kendall's Tau (K): 52.65. - [Open-ended QA Response Ranking on AlignBench Minos](): Open-ended QA Response Ranking; AlignBench Minos; K Score: 35.75. - [Open-ended QA Response Ranking on GaokaoBench Minos](): Open-ended QA Response Ranking; GaokaoBench Minos; K Score: 37.45. ## Original sources and implementation [Original paper]() [PDF]()