Papers with ELO
Personalized Benchmarking: Evaluating LLMs by Individual Preferences (2026.findings-acl)
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| Challenge: | Current benchmarks average preferences across all users to compute aggregate ratings . this overlooks individual user preferences when establishing model rankings . |
| Approach: | They compute personalized model rankings using ELO ratings and Bradley-Terry coefficients . they find users exhibit substantial heterogeneity in topical interests and communication styles . |
| Outcome: | The results show that individual rankings of LLM models diverge dramatically from aggregate rankings . a compact combination of topic and style features provides a useful feature space . |
ELO: Efficient Layer-Specific Optimization for Continual Pretraining of Multilingual LLMs (2026.eacl-industry)
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Hangyeol Yoo, ChangSu Choi, Minjun Kim, Seohyun Song, SeungWoo Song, Inho Won, Jongyoul Park, Cheoneum Park, KyungTae Lim
| Challenge: | Recent studies have focused on enhancing multilingual large language models (MLLMs) for specific languages. |
| Approach: | They propose an efficient layer-specific optimization method to enhance continual pretraining (CP) for specific languages in multilingual large language models (MLLMs). |
| Outcome: | The proposed method achieves a training speedup of up to 6.46 times compared to existing methods while improving target language performance by up to 5.2% on qualitative benchmarks. |
Re-evaluating Automatic LLM System Ranking for Alignment with Human Preference (2025.findings-naacl)
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| Challenge: | Evaluating and ranking the capabilities of different LLMs is crucial for understanding their performance and alignment with human preferences. |
| Approach: | They propose a system-level evaluation framework that ranks LLMs based on their alignment with human preferences. |
| Outcome: | The proposed framework aims to rank LLMs based on their performance and alignment with human preferences. |
Compare without Despair: Reliable Preference Evaluation with Generation Separability (2024.findings-emnlp)
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| Challenge: | a meta-evaluation measure, separability, estimates how suitable a test instance is for pairwise preference evaluation. |
| Approach: | They propose a measure of separability which measures how suitable a test instance is for pairwise preference evaluation. |
| Outcome: | The proposed measure shows that instances with high separability yield more consistent preference ratings from human- and auto-raters. |
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. |