Papers with RankNet
DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation (2025.findings-emnlp)
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| Challenge: | DeAR is an open-source framework that decouples the tasks of LLMs with holistic cross-document analysis. |
| Approach: | They propose an open-source framework that decouples relevance scoring with holistic cross-document analysis. |
| Outcome: | The proposed framework outperforms open-source frameworks in QA and open-domain QA. |
LCES: Zero-shot Automated Essay Scoring via Pairwise Comparisons Using Large Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to automate essay scoring rely on LLMs to generate absolute scores . however, these methods diverge from human evaluations due to model biases and inconsistent scoring . |
| Approach: | They propose a method that formulates AES as a pairwise comparison task using large language models. |
| Outcome: | The proposed method outperforms conventional zero-shot methods in accuracy while maintaining computational efficiency. |
Explanation Quality Assessment as Ranking with Listwise Rewards (2026.findings-acl)
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| Challenge: | a new approach to explanation quality assessment is to rank explanations by relative quality . standard reward objectives do not preserve graded distinctions well enough for policy optimization . |
| Approach: | They reformulate explanation quality assessment as a ranking problem instead of a generation problem . they train listwise and pairwise ranking models to preserve ordinal structure . |
| Outcome: | The proposed model outperforms regression on score separation and performance on listwise and pairwise models. |