Papers with CVR
Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and LLM-based Fine-Grained Job Descriptions (2025.emnlp-industry)
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| Challenge: | Existing talent search approaches fail to capture nuanced job-specific preferences and mitigate noise from subjective human judgments. |
| Approach: | They propose a framework that extracts fine-grained recruitment signals from job descriptions and historical hiring data and employs a role-aware multi-gate MoE network to capture behavioral differences across recruiter roles. |
| Outcome: | The proposed framework improves talent search effectiveness and delivers substantial business value. |
MotiR: Motivation-aware Retrieval for Long-Tail Recommendation (2025.acl-industry)
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| Challenge: | Existing methods for retrieval of recommendation systems rely on collaborative filtering signals and lacks similarity for long-tail items. |
| Approach: | They propose a Motivation-aware Retrieval for Long-Tail Recommendation that integrates purchase motivations with traditional item features to capture similarity among long-tail items. |
| Outcome: | The proposed model captures similarity between long-tail items while maintaining collaborative filtering advantages for popular items. |
Enhancing Advanced Visual Reasoning Ability of Large Language Models (2024.emnlp-main)
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| Challenge: | Recent advances in Vision-Language (VL) research have sparked new benchmarks for complex visual reasoning, challenging models’ advanced reasoning ability. |
| Approach: | They propose a novel multi-modal in-context learning methodology to enhance LLMs’ contextual understanding and reasoning. |
| Outcome: | The proposed model achieves SOTA performance among all visual reasoning tasks and achieves a 'higher level of accuracy' than previous models. |