Papers by Hengrui Zhang
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)
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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen
| Challenge: | Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy. |
| Approach: | They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges. |
| Outcome: | The proposed framework reduces retrieval time while maintaining high model performance. |
TABGEN-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation (2025.findings-acl)
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| Challenge: | Existing approaches to tabular data generation require fine-tuning, which is computationally expensive. |
| Approach: | They propose a new in-context learning framework to prompt a fixed LLM with in-constitut examples to enhance the in-text learning ability of LLMs for tabular data generation. |
| Outcome: | The proposed framework outperforms random selection strategies on five real-world tabular datasets and reduces error rate by 42.2% on fidelity metric. |
Recognizing Limits: Investigating Infeasibility in Large Language Models (2025.findings-emnlp)
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| Challenge: | Large language models fail to handle queries that exceed their knowledge and capabilities, leading to incorrect or fabricated responses. |
| Approach: | They conceptualize four main categories of infeasible tasks for LLMs which cover a broad spectrum of hallucination-related challenges identified in prior literature. |
| Outcome: | The proposed models can handle requests exceeding their knowledge and capabilities and refuse them . |
Beyond the Singular: Revealing the Value of Multiple Generations in Benchmark Evaluation (2026.findings-acl)
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| Challenge: | Existing evaluation methods for large language models overlook the inherent randomness of LLMs. |
| Approach: | They propose a hierarchical statistical model that incorporates both benchmark characteristics and LLM randomness to provide a more comprehensive representation of benchmarking process. |
| Outcome: | The proposed model improves the accuracy of estimating the benchmark score and reduces variance. |
PathwiseRAG: Multi-Dimensional Exploration and Integration Framework (2025.emnlp-main)
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| Challenge: | Existing retrieval-augmented generation systems employ rigid retrieval strategies . static retrieval produces knowledge blind spots, missing connections between quantum algorithms and encryption vulnerabilities . |
| Approach: | PathwiseRAG addresses these challenges through intent-aware strategy selection . it constructs a directed acyclic graph of interconnected sub-problems and explores multiple reasoning trajectories . |
| Outcome: | The proposed framework achieves higher accuracy and better reliability than current systems. |