Papers by Dante Everaert
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective (2025.naacl-long)
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Shenglai Zeng, Jiankun Zhang, Bingheng Li, Yuping Lin, Tianqi Zheng, Dante Everaert, Hanqing Lu, Hui Liu, Hui Liu, Yue Xing, Monica Xiao Cheng, Jiliang Tang
| Challenge: | Existing studies have shown that LLMs struggle to identify the boundaries of their own knowledge and tend to prioritize external information over internal knowledge learned during pre-training. |
| Approach: | They conduct a comprehensive analysis of LLM representation behaviors and demonstrate the significance of using representations in knowledge checking. |
| Outcome: | The proposed classifiers improve performance even when dealing with noisy knowledge databases. |
Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMs (2026.acl-long)
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Shenglai Zeng, Tianqi Zheng, Chuan Tian, Dante Everaert, Yau-Shian Wang, Yupin Huang, Michael J. Morais, Rohit Patki, Jinjin Tian, Xinnan Dai, Kai Guo, Monica Xiao Cheng, Hui Liu
| Challenge: | Existing approaches to personalize large language models (LLMs) rely on heuristic methods to compress user profiles but they ignore how LLMs process and prioritize different profile components. |
| Approach: | They propose an attention-guided context compression framework that leverages attention feedback from a marking model to mark important personalization sentences and guides a compression model to generate task-relevant compressed user contexts. |
| Outcome: | The proposed framework outperforms baselines across tasks, token limits, and settings while reducing token usage by 50 times. |
AmazonQAC: A Large-Scale, Naturalistic Query Autocomplete Dataset (2024.emnlp-industry)
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| Challenge: | Existing systems that provide a graphical representation of QAC are limited in their ability to provide real-time data. |
| Approach: | They introduce a new QAC dataset sourced from Amazon Search logs . they assess Prefix Trees, semantic retrieval, and Large Language Models with and without finetuning . |
| Outcome: | The proposed system can predict search terms based on user-typed prefixes . the proposed system achieves only half of what is theoretically possible on the test data . |
Retrieval Augmented Spelling Correction for E-Commerce Applications (2024.emnlp-industry)
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| Challenge: | e-commerce spelling correction services face a challenge with new brand names . we propose a new approach that uses a fine-tuned retrieval algorithm to correct for brand names. |
| Approach: | They propose a method that uses product names to be incorporated into a large language model to do contextual spelling correction. |
| Outcome: | The proposed approach improves performance with only minor latency increases . the proposed approach is more efficient than a stand-alone LLM . |