Papers by Bowei Zhang
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension (2020.coling-main)
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| Challenge: | Existing RC models focus on extractive or generative, but ignore integration of them. |
| Approach: | They propose a noisy user-generated text-oriented RC model that integrates extractive and generative RC models by a multi-task learning mechanism and an answer selection module. |
| Outcome: | The proposed model outperforms state-of-the-art models on Twitter. |
Distributed LLM Serving on Consumer-Grade GPUs by Reconciling Computation and Communication (2025.findings-emnlp)
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Lewei Jin, Kui Zhang, Yongqi Chen, null Zhuoyifan, Renjie Li, Yi Gao, Bowei Yang, Zhengong Cai, Wei Dong
| Challenge: | Large language models are reshaping internet services, and serving them is costly. |
| Approach: | They propose an efficient distributed LLM serving system that splits prefill and decode requests into smaller chunks . |
| Outcome: | The proposed system reduces TTFT, TPOT, and latency compared to the state-of-the-art system. |
Towards Trustworthy Smart Contract Synthesis: A Multi-Agent Framework with Lean-Based Verification (2026.acl-long)
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| Challenge: | Smart Contracts are the foundation of Decentralized Finance (DeFi), executing financial logic without trusted intermediaries. |
| Approach: | They propose a framework that integrates LLM-based generation with Lean-based auto-formalization and verification. |
| Outcome: | LeVer is the first trustworthy smart contract synthesis framework that integrates LLM-based generation with Lean-based auto-formalization and verification. |
Empowering Tree-structured Entailment Reasoning: Rhetorical Perception and LLM-driven Interpretability (2024.lrec-main)
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| Challenge: | Existing models for science question answering lack a framework for entailment trees . ambiguities and similarities between science facts complicate the fact retrieval process . |
| Approach: | They propose a framework for building entailment trees for science question answering . they propose to infuse knowledge that bridges the gap between reasoning types and rhetorical relations . |
| Outcome: | The proposed framework improves retrieval capabilities, understanding relationships and generating intermediate conclusions. |
Multi-grained Chinese Word Segmentation with Weakly Labeled Data (2020.coling-main)
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| Challenge: | Existing work on single-grained word segmentation (SWS) focuses on segmenting a sentence into multiple word sequences to preserve all words of different granularities. |
| Approach: | They propose to use a graph-based parser to accommodate weakly labeled data for MWS by employing a simple yet competitive graph-basic parsers with local loss. |
| Outcome: | The proposed model outperforms the state-of-the-art model on weakly labeled data on a high-quality dataset from canonical newswire (NEWS) and non-canonical web (BAIKE) data. |
Enhancing Event-centric News Cluster Summarization via Data Sharpening and Localization Insights (2025.acl-long)
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| Challenge: | Existing work on text summarization approaches are approaching or exceeding human excellence . |
| Approach: | They propose a framework that optimizes the balance between information volume and entropy in input texts. |
| Outcome: | The proposed framework optimizes information volume and entropy in input texts, achieving notable improvements in localized contexts. |
NILE: Internal Consistency Alignment in Large Language Models (2025.emnlp-main)
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Minda Hu, Qiyuan Zhang, Yufei Wang, Bowei He, Hongru Wang, Jingyan Zhou, Liangyou Li, Yasheng Wang, Chen Ma, Irwin King
| Challenge: | Recent advances show that the world knowledge in the Instruction Fine-Tuning (IFT) dataset, which is incompatible with LLMs’ internal knowledge, can greatly hurt the IFT performance. |
| Approach: | They propose a framework to optimize the effectiveness of IFT by carefully aligning the world and internal knowledge of LLMs. |
| Outcome: | The proposed framework can significantly improve performance across multiple LLM ability evaluation datasets. |
Comprehensive Abstractive Comment Summarization with Dynamic Clustering and Chain of Thought (2024.findings-acl)
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| Challenge: | Recent work on news comment summarization has focused on extractive methods within constraints. |
| Approach: | They propose an enhanced fast clustering algorithm that maintains a dynamic similarity threshold to ensure high density of each comment cluster being built. |
| Outcome: | The proposed method improves the baseline methods and the test suite on real-world news comments. |