Papers with rank loss
Best Practices for Distilling Large Language Models into BERT for Web Search Ranking (2025.coling-industry)
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| Challenge: | Recent studies have highlighted the potential of Large Language Models (LLMs) as zero-shot relevance rankers. |
| Approach: | They propose to use a ranking loss to transfer ranking knowledge from LLMs to smaller models like BERT. |
| Outcome: | The proposed model has been successfully integrated into a commercial web search engine as of February 2024. |
RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human Feedback (2025.coling-main)
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| Challenge: | Existing methods to mitigate hallucinations generate erroneous or fabricated information. |
| Approach: | They propose a rank-response-based model that annotates pair-reponses and trains alignment algorithms to improve the correspondence between images and text. |
| Outcome: | The proposed model outperforms the DPO method and outperfies existing methods on two MLLMs of different sizes and four widely used benchmarks. |
Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction (2024.lrec-main)
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| Challenge: | Existing methods for keyphrase extraction lack the ability to utilize keyphrase information, which may result in biased results. |
| Approach: | They propose a keyphrase extraction task that leverages the supervised Variational Information Bottleneck to guide the text diffusion process for generating enhanced keyphrase representations. |
| Outcome: | The proposed keyphrase extraction model outperforms existing methods on open domain keyphrase extractor benchmark and scientific domain dataset. |