Papers by Qiwei Zhao
Hypergraph based Understanding for Document Semantic Entity Recognition (2024.acl-long)
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| Challenge: | Existing document understanding models focus on entity categories while ignoring the extraction of entity boundaries. |
| Approach: | They propose a hypergraph attention document semantic entity recognition framework which uses hypergraph focus to focus on entity boundaries and entity categories at the same time. |
| Outcome: | The proposed framework can improve the performance of existing models on FUNSD, CORD, XFUND and SROIE. |
Can Large Language Models Be Good Language Teachers? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have achieved remarkable success across diverse domains, but their potential as effective language teachers remains inadequately assessed. |
| Approach: | They propose a framework to evaluate Chinese language teachers' pedagogical competence against international standards. |
| Outcome: | The proposed framework evaluates 13 latest multilingual and Chinese LLMs against international standards for Chinese language teachers. |
Faster In-Context Learning for LLMs via N-Gram Trie Speculative Decoding (2025.emnlp-main)
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| Challenge: | In-Context Learning (ICL) is a key method in prompt engineering, but its long retrieved contexts and limited token throughput will slow reasoning speeds. |
| Approach: | They propose a method that leverages the overlap between context and model output to generate drafts from the context. |
| Outcome: | The proposed method achieves the highest mean speedup on Vicuna-7B, Llama2-7B-Chat, and Llma3-8B-Instruct tasks. |
Faster MoE LLM Inference for Extremely Large Models (2026.findings-acl)
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| Challenge: | Existing inference optimizations for coarse-grained Mixture-of-Experts models implicitly assume a fixed activation budget, which is poorly understood. |
| Approach: | They propose a training-free policy that adapts token-level activation using router confidence and entropy while remaining within the model’s original budget. |
| Outcome: | The proposed skipping policy can provide substantial throughput gains, but optimal static schedules vary significantly across models and routing mechanisms. |
BoYaEval: Evaluating Multimodal Large Language Models on Understanding Ancient Chinese Musical Scores (2026.acl-long)
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| Challenge: | Multimodal Large Language Models excel in general tasks but struggle with specialized, structured cultural symbols. |
| Approach: | They evaluate 21 leading MLLMs and compare their performance to a benchmark for Ancient Chinese musical notation. |
| Outcome: | The benchmark evaluates 21 leading MLLMs on five types of ancient Chinese music notation systems. |
Uncertainty Propagation on LLM Agent (2025.acl-long)
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Qiwei Zhao, Dong Li, Yanchi Liu, Wei Cheng, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Huaxiu Yao, Chen Zhao, Haifeng Chen, Xujiang Zhao
| Challenge: | Existing methods for estimating uncertainty in large language models (LLMs) focus on final-step outputs, which fail to account for cumulative uncertainty over multi-step decision-making process and dynamic interactions between agents and their environments. |
| Approach: | They propose a framework that propagates uncertainty through each step of an LLM-based agent’s reasoning process. |
| Outcome: | Extensive experiments on benchmark datasets show that the proposed framework outperforms state-of-the-art methods by 20%. |
GoT-R1: Internalizing Graph-of-Thought via Structural Reinforcement for High-Density Reasoning (2026.findings-acl)
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| Challenge: | Chain-of-Thought reasoning suffers from an inherent mechanism flaw: linearity induces overthinking . emergence of Large Language Models (LLMs) has fundamentally redefined artificial intelligence . |
| Approach: | They propose a framework that replaces verbose linear trajectories with high-density reasoning graphs. |
| Outcome: | The proposed framework outperforms state-of-the-art models with reduced token overhead. |
Dialogue-RAG: Enhancing Retrieval for LLMs via Node-Linking Utterance Rewriting (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) methods have demonstrated significant potential on tasks across multiple domains. |
| Approach: | They propose a lightweight IUR model for query rewriting to complete key information in dialogue to enhance retrieval. |
| Outcome: | The proposed model improves retrieval and generation ability of RAG system in multi-round dialogue scenarios. |