Papers with digital platforms
Text Style Transfer for Bias Mitigation using Masked Language Modeling (2022.naacl-srw)
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| Challenge: | Various research findings have concluded that biased textual data has significant effects on target demographic groups. |
| Approach: | They propose a text-style transfer model that can be trained on non-parallel data and be used to automatically mitigate bias in textual data. |
| Outcome: | The proposed model improves on limitations of existing methods while maintaining good style transfer accuracy. |
I-SEE: An Instruction-tuned, SOP-Enhanced Quality Evaluator for Product Content (2025.emnlp-industry)
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| Challenge: | Existing approaches to content evaluation treat information uniformly without prioritizing based on customer relevance. |
| Approach: | They propose a framework that combines domain expertise with a single instruction to improve content. |
| Outcome: | a new framework outperforms existing models in detecting inconsistencies across 20 product categories and 150 product specific features. |
Consistency-Aware Online Multi-Objective Alignment for Related Search Query Generation (2025.acl-industry)
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| Challenge: | Existing methods fail to reconcile click-through rate (CTR) optimization with topic expansion. |
| Approach: | They propose a query generation framework that aligns click-through rate and topic expansion goals through an online DPO paradigm. |
| Outcome: | The proposed approach achieves significant CTR gains (+2.3%) and higher human-rated query quality compared to state-of-the-art methods. |
ReList: A Multi-objective Reasoning Framework for Diversified Listwise Query Recommendation (2026.acl-industry)
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| Challenge: | Existing methods for related search have limited semantic redundancy and wasted retrieval quota . generative retrieval approaches lack explicit reasoning, relying on superficial click-through rate rewards . |
| Approach: | They propose a framework that transforms related search into a reasoning-enhanced listwise generation task. |
| Outcome: | Experimental results show that ReList outperforms state-of-the-art methods in query diversity and user engagement. |
Nuanced Toxicity Detection in Spanish: A New Corpus and Benchmark Study (2026.findings-eacl)
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Alba María Mármol-Romero, Robiert Sepúlveda-Torres, Estela Saquete, María-Teresa Martín-Valdivia, L. Alfonso Ureña
| Challenge: | Existing corpora for Spanish are under-resourced for toxic content detection . sarcasm, indirect aggression, irony, and other toxicity are not detected in English . |
| Approach: | They propose to extend the NECOS-TOX corpus to include 4,011 Spanish comments . each comment is annotated across three levels of toxicity, with substantial inter-annotator agreement . |
| Outcome: | The proposed model performs on par with larger models and is released publicly . the proposed model is based on a human-in-the-loop active learning strategy . |
ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignment (2025.emnlp-main)
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Zhipeng Bian, Jieming Zhu, Qijiong Liu, Wang Lin, Guohao Cai, Zhaocheng Du, Jiacheng Sun, Zhou Zhao, Zhenhua Dong
| Challenge: | Large language models and diffusion models have opened new possibilities for AI-generated content . personalized cover image generation remains underexplored despite its critical role in boosting user engagement on digital platforms. |
| Approach: | They propose a framework that integrates MLLM-based prompting with personalized preference alignment to generate high-quality, contextually relevant covers. |
| Outcome: | The proposed framework improves image quality, semantic fidelity, and personalization, leading to stronger user appeal and offline recommendation accuracy in downstream tasks. |
Tunable LLM-based Proactive Recommendation Agent (2025.acl-long)
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| Challenge: | Current methods focus on catering to existing user interests, leading to polarized recommendation distributions. |
| Approach: | They propose an LLM-based Actor-Critic Agent framework to cultivate latent interests through multi-step recommendations. |
| Outcome: | The proposed framework optimizes long-term rewards and dynamically evolves with user feedback. |