Papers by Tianqi Zheng
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 . |
Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation (2025.emnlp-main)
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| Challenge: | Large Multimodal Models (LMMs) can process text, images, and audio, but they introduce privacy vulnerabilities. |
| Approach: | They propose a compositional structured prompt attack to exploit MRAG privacy vulnerabilities . they show that LMMs can generate outputs resembling retrieved content . |
| Outcome: | The proposed approach generates outputs resembling retrieved content and exposes sensitive information. |
Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) enhanced with external contexts face challenges in handling imperfect evidence. |
| Approach: | They propose a framework that can balance internal knowledge with external contexts . they propose gating mechanisms and low-rank representation adapters to adjust hidden representations based on a lightweight intervention function . |
| Outcome: | The proposed model can effectively balance internal knowledge with external context, similar to human cognitive processes. |
Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data (2025.emnlp-main)
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Shenglai Zeng, Jiankun Zhang, Pengfei He, Jie Ren, Tianqi Zheng, Hanqing Lu, Han Xu, Hui Liu, Yue Xing, Jiliang Tang
| Challenge: | Existing literature suggests that RAG systems may face privacy issues when the retrieval process involves private data. |
| Approach: | They propose a two-stage synthetic data generation paradigm that uses attributes to preserve contextual information from the original data. |
| Outcome: | The proposed approach preserves key contextual information from the original data while reducing privacy risks. |