Papers by Shiwei Zhang
Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization (2024.findings-acl)
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| Challenge: | Multimodal Summarization with Multimodal Output (MSMO) is a new approach to produce a multimodal summary that integrates both text and relevant images. |
| Approach: | They propose an Entity-Guided Multimodal Summarization model that integrates both text and relevant images to produce a multimodal summary. |
| Outcome: | The proposed model integrates text-image and entity-image information and refines image selection through knowledge distillation from a pre-trained vision-language model. |
Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models (2025.naacl-long)
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Hengyi Wang, Haizhou Shi, Shiwei Tan, Weiyi Qin, Wenyuan Wang, Tunyu Zhang, Akshay Nambi, Tanuja Ganu, Hao Wang
| Challenge: | Multimodal Large Language Models have shown significant promise in various applications, but a comprehensive evaluation of their long-context capabilities remains underexplored. |
| Approach: | They propose a benchmark to assess the long-context capabilities of multimodal large language models. |
| Outcome: | The proposed benchmark compared MLLMs with API-based and open-source models in a long-context scenario. |
An Empirical Study of Sentiment-Enhanced Pre-Training for Aspect-Based Sentiment Analysis (2023.findings-acl)
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| Challenge: | Existing approaches to Aspect-Based Sentiment Analysis (ABSA) are lacking in a comprehensive evaluation and fair comparison. |
| Approach: | They propose to use a knowledge-mining method to build a large-scale knowledge-annotated SPT corpus and integrate sentiment knowledge into pre-training. |
| Outcome: | The proposed method is able to build a large-scale knowledge-annotated SPT corpus and compares with other methods. |
Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction (2024.acl-long)
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| Challenge: | Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review. |
| Approach: | They propose a self-training framework with a pseudo-label scorer to assess the match between reviews and their pseudo-labels and train a generative model on it. |
| Outcome: | The proposed framework can predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, and it can significantly improve self-training. |
ClinAlign: Scaling Healthcare Alignment from Clinician Preference (2026.findings-acl)
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Shiwei Lyu, Xidong Wang, Hao Zhu, Lei Liu, Chaohe Zhang, Jian Wang, Jinjie Gu, Benyou Wang, Yue Shen
| Challenge: | Existing methods for aligning open-ended outputs with fine-grained clinician preferences are weakly grounded in professional guidelines. |
| Approach: | They propose a framework to align large language models' outputs with fine-grained clinician preferences . they propose 119 broadly reusable, clinically grounded principles organized by clinical dimensions . |
| Outcome: | The proposed framework outperforms existing models on HealthBench-Hard and Deepseek-R1 and o3. |
Boundary-Driven Table-Filling for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| Challenge: | Existing work focuses on extracting aspect terms and opinion terms without considering the relations between aspect terms . |
| Approach: | They propose a task to extract aspect terms, opinion terms, and expressed sentiments from a two-dimensional (2D) table. |
| Outcome: | The proposed method achieves state-of-the-art on several public benchmarks and is well-suited to the ASTE task. |
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)
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Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang
| Challenge: | Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools. |
| Approach: | They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems. |
| Outcome: | The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems. |
Variational Language Concepts for Interpreting Foundation Language Models (2024.findings-emnlp)
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| Challenge: | Foundation Language Models (FLMs) have achieved remarkable success in natural language processing. |
| Approach: | They propose a variational Bayesian framework to provide word-level interpretations for FLMs . they propose valc to find optimal language concepts to interpret FLM predictions . |
| Outcome: | Empirical results show that the proposed framework can provide conceptual interpretations for foundation language models. |
ReAL: How Can LLMs Simulate the Real Teacher? Retrieval-enhanced Agent for Adaptive Learning (2025.findings-emnlp)
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| Challenge: | Prior methods model learner-item interactions based only on ID sequences, leading to insufficient use of both learner and item information. |
| Approach: | They propose a Retrieval-enhanced Agent for Adaptive Learning powered by large language models to simulate teacher decision-making with extensive prior knowledge and teaching experience. |
| Outcome: | The proposed model outperforms existing models on three real-world datasets in both internal and external perspectives. |
Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction (2023.emnlp-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is an important task in sentiment analysis, but data scarcity limits performance of existing methods. |
| Approach: | They propose a target-to-source augmentation approach to alleviate the issue of data scarcity in Aspect Sentiment Triplet Extraction (ASTE) they use fluency and alignment discriminators to provide feedback and use this feedback to optimize the generator. |
| Outcome: | The proposed approach significantly improves the performance of existing methods. |
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents (2025.findings-naacl)
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Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) fail to effectively guide the planning trajectories during task solving and result in planning hallucinations. |
| Approach: | They propose a novel approach to enhance the planning capabilities of large language models by incorporating explicit action knowledge. |
| Outcome: | The proposed approach can achieve comparable or superior performance to existing baselines on HotpotQA and ALFWorld. |
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words (2022.coling-1)
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Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu
| Challenge: | Pre-trained models perform poorly with limited data and rare biomedical words. |
| Approach: | They propose to use prompt to fine-tune pre-trained models for biomedical domain tuning with a simple approach. |
| Outcome: | The proposed method achieves up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings. |