Papers by Shenglai Zeng
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. |
Empowering GraphRAG with Knowledge Filtering and Integration (2025.emnlp-main)
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| Challenge: | Large language models suffer from knowledge gaps and hallucinations, resulting in incorrect or poor reasoning. |
| Approach: | They propose Graph retrieval-augmented generation (GraphRAG) which integrates structured knowledge from external graphs to enhance model's reasoning. |
| Outcome: | Experiments on knowledge graph QA tasks show that GraphRAG significantly improves reasoning performance across multiple backbone models. |
Exploring Memorization in Fine-tuned Language Models (2024.acl-long)
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Shenglai Zeng, Yaxin Li, Jie Ren, Yiding Liu, Han Xu, Pengfei He, Yue Xing, Shuaiqiang Wang, Jiliang Tang, Dawei Yin
| Challenge: | Existing studies have shown that pre-trained langauge models tend to memorize and regenerate segments of their pre-training corpus when prompted appropriately. |
| Approach: | They conduct the first comprehensive analysis to explore language models’ memorization during fine-tuning across tasks. |
| Outcome: | The proposed analysis shows that memorization presents a strong disparity among different fine-tuning tasks. |
The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) (2024.findings-acl)
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Shenglai Zeng, Jiankun Zhang, Pengfei He, Yiding Liu, Yue Xing, Han Xu, Jie Ren, Yi Chang, Shuaiqiang Wang, Dawei Yin, Jiliang Tang
| Challenge: | Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model generation with proprietary and private data, where data privacy is . a privacy issue that is currently under-explored, is posed by RAG. |
| Approach: | They propose to use retrieval-augmented generation (RAG) to facilitate language model generation with proprietary and private data where data privacy is a pivotal concern. |
| Outcome: | The proposed attack methods demonstrate that RAG can mitigate the old risks, i.e., leakage of the LLMs’ training 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. |
On the Generalization of Training-based ChatGPT Detection Methods (2024.findings-emnlp)
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| Challenge: | Existing studies show that training-based methods are ineffective to detect LLM generated texts from unseen tasks or topics which are not collected during training. |
| Approach: | They propose to train classification models to distinguish LLMs from human texts by a distribution shift caused by prompts, text lengths, topics, and language tasks. |
| Outcome: | The proposed methods can detect LLMs from black-box models, but they suffer from distribution shifts due to a wide range of factors, including prompts, text lengths, topics, and language tasks. |
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. |
Unveiling Privacy Risks in LLM Agent Memory (2025.acl-long)
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| Challenge: | Large Language Model (LLM) agents store private user-agent interactions in memory for demonstrations, introducing new privacy risks for LLM agents. |
| Approach: | They propose an attack that extracts private information from memory under a black-box setting and propose a method that can be used to attack the agent. |
| Outcome: | The proposed attack is effective under a black-box setting and it is demonstrated on two representative agents. |
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. |