Papers with head-to-head comparisons
Re-Evaluating Evaluation for Multilingual Summarization (2024.emnlp-main)
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Jessica Forde, Ruochen Zhang, Lintang Sutawika, Alham Aji, Samuel Cahyawijaya, Genta Winata, Minghao Wu, Carsten Eickhoff, Stella Biderman, Ellie Pavlick
| Challenge: | Existing studies have shown that automated evaluation approaches correlate with human ratings in English, but this is unclear for other languages. |
| Approach: | They construct a small-scale pilot dataset containing article-summary pairs and human ratings in English, Chinese and Indonesian to measure the strength of summaries. |
| Outcome: | The results show that standard metrics are unreliable measures of quality in Chinese and Indonesian. |
Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat (2025.acl-long)
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| Challenge: | Evaluating large language models (LLMs) is a complex task. Pairwise ranking has emerged as state-of-the-art method to evaluate human preferences. |
| Approach: | They propose to use pairwise ranking to evaluate human preferences . they propose to evaluate the robustness of ranking algorithms in LLMs . |
| Outcome: | The proposed methods are based on the principles of effective ranking and the robustness of several ranking algorithms in the context of LLMs. |