Papers with Normalized Discounted Cumulative Gain
Permutative Preference Alignment from Listwise Ranking of Human Judgments (2025.emnlp-main)
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| Challenge: | Existing methods to align Large Language Models with human preferences are based on the Bradley-Terry model, but when multiple responses are available, the B-T model fails to guarantee an accurate list ranking of the responses. |
| Approach: | They propose an offline listwise approach that incorporates the Normalized Discounted Cumulative Gain (NDCG) as an alternative training objective for LLM alignment. |
| Outcome: | The proposed approach outperforms existing pairwise and listwise methods on evaluation sets and general benchmarks such as AlpacaEval. |
MOSAIC: Masked Objective with Selective Adaptation for In-domain Contrastive Learning (2026.findings-eacl)
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| Challenge: | a new framework for domain adaptation of text embedding models addresses the challenges of adapting general-domain text embeds to specialized domains. |
| Approach: | They propose a framework for domain adaptation of text embedding models that integrates masked supervision and mangled objectives within a unified training pipeline. |
| Outcome: | The proposed framework improves on high-resource and low-resourced domains while preserving the robustness of the original model. |