Papers by Sijia Wang
Targeted Augmentation for Low-Resource Event Extraction (2024.findings-naacl)
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| Challenge: | Existing methods for low-resource information extraction struggle to strike a balance between weak augmentation and drastic augmentation. |
| Approach: | They propose a data augmentation paradigm that uses back validation and targeted augmentation to produce augmented examples with enhanced diversity, polarity, accuracy, and coherence. |
| Outcome: | The proposed paradigm produces augmented examples with enhanced diversity, polarity, accuracy, and coherence. |
Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts (2026.acl-long)
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Sijia Luo, Xiaokang Zhang, Yuxuan Hu, Bohan Zhang, Ke Wang, Jinbo Su, Mengshu Sun, Lei Liang, Jing Zhang
| Challenge: | Existing methods for storing key-value caches during long-horizon rollouts cause performance collapses. |
| Approach: | They propose a new training paradigm that empowers stable RL training under sparse rollouts. |
| Outcome: | The proposed model reduces rollout overhead while maintaining the performance. |
Self-Guided Function Calling in Large Language Models via Stepwise Experience Recall (2025.findings-emnlp)
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| Challenge: | Existing methods for function calling require expert effort and prompt engineering becomes inefficient. |
| Approach: | They propose a method that performs fine-grained, stepwise retrieval from a continually updated experience pool. |
| Outcome: | The proposed method achieves an average improvement of 6.1% on easy and 4.7% on hard questions. |
Ameli: Enhancing Multimodal Entity Linking with Fine-Grained Attributes (2024.eacl-long)
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| Challenge: | Experimental results show that understanding attributes of mentions from text descriptions and visual images plays a vital role in multimodal entity linking. |
| Approach: | They propose to integrate attributes into multimodal entity linking using a text-image-based knowledge base. |
| Outcome: | The proposed approach integrates attributes into disambiguation. |
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2022.findings-acl)
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| Challenge: | Existing approaches to event extraction are limited to a set of pre-defined types. |
| Approach: | They propose a natural language query framework that uses event types and argument roles to extract candidate triggers and arguments from input text. |
| Outcome: | The proposed framework outperforms existing methods on zero-shot event extraction. |
Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing detectors use classifier-style probability signals or rely on rewriting, which can degrade quality and introduce new triggers. |
| Approach: | They propose to efficiently remove poisoned examples before or during fine-tuning . |
| Outcome: | The proposed method outperforms prior detectors on two machine translation datasets and one QA dataset. |
Benchmarking Diverse-Modal Entity Linking with Generative Models (2023.findings-acl)
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Sijia Wang, Alexander Hanbo Li, Henghui Zhu, Sheng Zhang, Pramuditha Perera, Chung-Wei Hang, Jie Ma, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Bing Xiang, Patrick Ng
| Challenge: | Existing models for diverse-mode entity linking (EL) work well on per modality configurations, but it is more challenging to design a unified model for diverse modality. |
| Approach: | They propose a generative diverse-modal model that integrates text, image and table . they propose combining a multimodal encoder-decoder paradigm with a fine-tuning GDMM . |
| Outcome: | The proposed model outperforms state-of-the-art models by 8.51 F1 on average for diverse-modal EL. |
The Art of Prompting: Event Detection based on Type Specific Prompts (2023.acl-short)
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| Challenge: | Experimental results show that a well-defined and comprehensive description of event types can significantly improve event detection performance when the annotations are limited. |
| Approach: | They propose a unified framework to integrate event type specific prompts for supervised, few-shot and zero-shot event detection. |
| Outcome: | The proposed framework shows up to 22.2% gain over the prior state-of-the-art frameworks. |
Improve Decoding Factuality by Token-wise Cross Layer Entropy of Large Language Models (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) often struggle with the issue of generating inaccurate or fabricated content even when they possess correct knowledge. |
| Approach: | They propose a decoding method that mitigates hallucinations without extra training . they propose entropy eNhanced decoding that leverages inner probability changes . |
| Outcome: | The proposed method improves the truthfulness and informativeness of generation while maintaining robust QA accuracy. |
Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills (2025.emnlp-main)
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Changsheng Wang, Chongyu Fan, Yihua Zhang, Jinghan Jia, Dennis Wei, Parikshit Ram, Nathalie Baracaldo, Sijia Liu
| Challenge: | Existing methods for LRM unlearning overlook critical information leakage in reasoning traces, even when final answers are successfully removed. |
| Approach: | They propose a method that suppresses reasoning traces while preserving the model's general reasoning ability. |
| Outcome: | The proposed method significantly reduces reasoning trace leakage and achieves strong performance across reasoning and safety benchmarks, including WMDP, StrongReject, JBB-Behaviors and WildJailbreak. |
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction (2022.naacl-main)
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| Challenge: | Existing models are vulnerable to adversarial attacks, but their vulnerability is underexplored. |
| Approach: | They propose to concatenate a perturbed but semantically similar tweet into a model that fools stock prediction models. |
| Outcome: | The proposed method achieves consistent success rates and causes significant monetary loss in trading simulation by simply concatenating a perturbed but semantically similar tweet. |
RE2: Region-Aware Relation Extraction from Visually Rich Documents (2024.naacl-long)
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| Challenge: | Existing studies on relation extraction from visually rich documents focus on layout structure and Optical Character Recognition (OCR) results. |
| Approach: | They propose a relation extraction tool that leverages layout structure among entity blocks to improve relation prediction. |
| Outcome: | The proposed model outperforms existing models on a wide range of domains and languages. |
Beyond the Surface: A Solution-Aware Retrieval Model for Competition-level Code Generation (2025.findings-emnlp)
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| Challenge: | Existing retrieval models emphasize surface-level semantic similarity, neglecting deeper solution-level logical similarities. |
| Approach: | They propose a solution-aware ranking model empowered by synthetic data for competitive programming tasks. |
| Outcome: | The proposed ranking model outperforms existing retrieval models in precision and recall metrics. |
More Samples or More Prompts? Exploring Effective Few-Shot In-Context Learning for LLMs with In-Context Sampling (2024.findings-naacl)
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Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Yisi Sang, Sijia Liu, James Hendler, Dakuo Wang
| Challenge: | Existing studies on LLM prompting focus on selecting a better set of data samples inside one single prompt input, but why not design and leverage multiple ICL prompts together to further improve the LLM’s performance? |
| Approach: | They propose a low-resource LLM prompting technique to optimize the construction of multiple ICL prompt inputs to produce confident predictions. |
| Outcome: | The proposed technique can produce confident predictions by optimizing the construction of multiple ICL prompt inputs on four NLI datasets and one QA dataset. |
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good (P19-1)
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| Challenge: | Persuasion agents are a form of communication that can be used to change people's opinions and actions for social good. |
| Approach: | They designed an online persuasion task where one participant was asked to persult the other to donate to a specific charity. |
| Outcome: | The proposed system could change people's opinions and actions for social good. |
SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection (2026.acl-industry)
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Zhiyong Cao, Dunqiang Liu, Qi Dai, Haojun Xu, Huai Yuen Khor, Hao Wang, Huan He, Yafei Liu, Ke Ma, Ruqian Shi, Sicheng Zhou, Sijia Yao
| Challenge: | High-quality data in training proactive dialogue agents is scarce, despite fine-tuning and reinforcement learning . a recent study has shown that the effectiveness of supervised fine-touring is limited by the lack of high-quality, domain-specific training data. |
| Approach: | They propose a framework for training recruitment proactive dialogue agents using a high-fidelity user simulator and a multi-dimensional evaluation framework based on Chain-of-Intention. |
| Outcome: | The proposed framework outperforms existing simulator-based data selection strategies in a real-world recruitment scenario. |
Do LLMs Know and Understand Domain Conceptual Knowledge? (2025.findings-emnlp)
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| Challenge: | Concept sememe tree is a hierarchical structure that represents lexical meaning by combining sememes and their relationships. |
| Approach: | They introduce a Neighbor Semantic Structure (NSS) and a Chain-of-Thought prompting method to evaluate the effectiveness of various Large Language Models (LLMs) in generating concept sememe trees. |
| Outcome: | The proposed method guides LLMs through an analysis of a term’s intrinsic core concepts, essential attributes, and semantic relationships, enabling the generation of concept sememe trees. |
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction (2024.findings-emnlp)
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| Challenge: | Experimental results show a significant performance gap between tuning-based approaches and event extraction approaches. |
| Approach: | They propose a debate as optimization system where the primary objective is to iteratively refine the large language models outputs through debating without parameter tuning. |
| Outcome: | The proposed system reduces performance gap between supervised approaches and tuning-free methods by 18.1% and 17.8% on ACE05 and 17.9% and 15.2% on CASIE respectively. |
Annotate Chinese Aspect with UMR——a Case Study on the Liitle Prince (2024.lrec-main)
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| Challenge: | Uniform Meaning Representation (UMR) is a graphbased cross-linguistically applicable semantic representation that allows for deep semantic analysis. |
| Approach: | They propose to use an aspectual lattice to adapt to different languages and design values that encompass both viewpoint aspect and situation aspect. |
| Outcome: | The proposed representations are based on the Chinese version of The Little Prince and are compared with other representations. |