Papers with Real-world
Hierarchical Document Refinement for Long-context Retrieval-augmented Generation (2025.acl-long)
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Jiajie Jin, Xiaoxi Li, Guanting Dong, Yuyao Zhang, Yutao Zhu, Yongkang Wu, Zhonghua Li, Ye Qi, Zhicheng Dou
| Challenge: | Real-world RAG applications often encounter long-context input scenarios where redundant information and noise results in higher inference costs and reduced performance. |
| Approach: | They propose an efficient plug-and-play refiner that leverages the structural characteristics of long documents. |
| Outcome: | Experiments on seven QA datasets show that LongRefiner achieves competitive performance in various scenarios while using 10x fewer computational costs and latency compared to baseline. |
Web Intellectual Property at Risk: Preventing Unauthorized Real-Time Retrieval by Large Language Models (2025.emnlp-main)
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| Challenge: | a new framework protects web content from unauthorized LLM real-time extraction and redistribution . multiple AI companies have been accused of scraping digital IP for proprietary benefit . |
| Approach: | They propose a defense framework that empowers web content creators to safeguard their web-based IP from unauthorized LLM real-time extraction and redistribution by leveraging the semantic understanding capability of LLMs themselves. |
| Outcome: | The proposed defense outperforms traditional defenses on LLMs and improves on black-box optimization problems. |
BERTwich: Extending BERT’s Capabilities to Model Dialectal and Noisy Text (2023.findings-emnlp)
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| Challenge: | Pre-trained language models like BERT deteriorate in the face of dialect variation or noise. |
| Approach: | They propose to sandwich BERT's encoder stack between additional encoder layers trained to perform masked language modeling on noisy text. |
| Outcome: | The proposed approach promotes zero-shot transfer to dialectal text and reduces embedding space between words and noisy counterparts. |