Papers by Jiasheng Si

D2-RAG: Dual-Decision Retrieval-Augmented Generation via Multi-Dimensional Uncertainty and Utility-Aware Decoding (2026.findings-acl)

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Jinshuo Zhang, Xiaoding Zhou, Weiyu Zhang, Guoqiang Chen, Ying Lian, Xiaoyang Meng, Yonghe Chen, Hongjiao Guan, Jiasheng Si, Wenpeng Lu

Information Retrieval

Challenge:	Retrieval-Augmented Generation (RAG) mitigates hallucinations in large language models by incorporating external knowledge.
Approach:	They propose a dual-decision retrieval-augmented generation that integrates multi-dimensional uncertainty estimation to decide whether to retrieve and employs adaptive contrastive decoding to handle retrieved contexts of varying quality.
Outcome:	The proposed model outperforms baselines on four medical question-answering datasets while suppressing interference from noisy contexts.

EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification (2023.emnlp-main)

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Yingjie Zhu, Jiasheng Si, Yibo Zhao, Haiyang Zhu, Deyu Zhou, Yulan He

Responsible & Trustworthy NLP

Challenge:	Existing methods to augment training data with counterfactuals fail to handle multi-hop fact verification due to their incapability to preserve complex logical relationships.
Approach:	They propose to augment training data with counterfactuals that alter causal features of the original data by preserving logical relationships.
Outcome:	The proposed method outperforms the baselines and can generate linguistically diverse counterfactuals without disrupting their logical relationships.

Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification (2021.acl-long)

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Jiasheng Si, Deyu Zhou, Tongzhe Li, Xingyu Shi, Yulan He

Reasoning

Challenge:	Existing methods for fact verification focus on analyzing semantic interaction between claim and evidence but fail to capture their topical consistency . Existing models focus on the aggregation of multiple pieces of evidence without considering their implicit stances to the claim, thereby introducing spurious information.
Approach:	They propose a topic-aware evidence reasoning and stance-again aggregation model that checks topical consistency between claims and evidence.
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets.

Denoising Rationalization for Multi-hop Fact Verification via Multi-granular Explainer (2024.findings-emnlp)

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Jiasheng Si, Yingjie Zhu, Wenpeng Lu, Deyu Zhou

Responsible & Trustworthy NLP

Challenge:	Existing rationalization methods for multi-hop fact verification lack nuanced composition in the evidence, which leads to noise rationalization.
Approach:	They propose a method to obtain rationale by completely removing subset of input without compromising verification accuracy.
Outcome:	The proposed method outperforms 12 baselines on three multi-hop fact verification datasets.

Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information (2024.findings-emnlp)

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Yongheng Zhang, Qiguang Chen, Jingxuan Zhou, Peng Wang, Jiasheng Si, Jin Wang, Wenpeng Lu, Libo Qin

Reasoning

Challenge:	Chain-of-Thought (CoT) is a key technique for enhancing the performance of Large Language Models.
Approach:	They propose a framework that optimizes outputs by utilizing wrong information and multi-perspective verification.
Outcome:	The proposed framework surpasses all baselines on 8 datasets and 5 LLMs.

Plan Dynamically, Express Rhetorically: A Debate-Driven Rhetorical Framework for Argumentative Writing (2025.emnlp-main)

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Xueguan Zhao, Wenpeng Lu, Chaoqun Zheng, Weiyu Zhang, Jiasheng Si, Deyu Zhou

Linguistic Theories

Challenge:	Argumentative essay generation (AEG) is a complex task that requires advanced semantic understanding, logical reasoning, and organized integration of perspectives.
Approach:	They propose a debate-driven rhetorical framework for argumentative writing that integrates Bitzer’s rhetorical situation theory to improve logical depth, argumentative diversity, and rhetorical persuasiveness.
Outcome:	The proposed framework improves logical depth, argumentative diversity, and rhetorical persuasiveness over existing state-of-the-art models.

Beyond Static Artifacts: An Evolutionary Framework for Synthetic Claim Generation (2026.acl-long)

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Yeqing Teng, Jiasheng Si, Shuxia Lin, Linhai Zhang, Weiyu Zhang, Wenpeng Lu, Deyu Zhou, Xiaoming Wu

Challenge:	Existing claim detection benchmarks treat claims as static textual artifacts . current research ignores sociological etiology of how information naturally emerges and mutates .
Approach:	They propose a socially generative framework for synthetic claim generation . they propose utterance, proposition and context-based simulations to capture truth decay .
Outcome:	The proposed paradigm models claims as socially evolving entities . it allows precise simulation of truth decay and intervened propagation with multi-auditor oversight .

ClimateViz: A Benchmark for Statistical Reasoning and Fact Verification on Scientific Charts (2025.emnlp-main)

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Ruiran Su, Jiasheng Si, Zhijiang Guo, Janet B. Pierrehumbert

Responsible & Trustworthy NLP

Challenge:	Scientific fact-checking has largely focused on textual and tabular sources, neglecting scientific charts.
Approach:	They propose a benchmark for scientific fact-checking grounded in scientific charts . climateViz comprises 49,862 claims paired with 2,896 visualizations . results show current models struggle to perform fact- checking when statistical reasoning is required .
Outcome:	The climateviz benchmark is the first large-scale benchmark for scientific fact-checking . it includes 49,862 claims paired with 2,896 visualizations labeled as support, refute, or not enough .

Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information (2024.findings-emnlp)

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Guobiao Zhang, Xueping Peng, Tao Shen, Guodong Long, Jiasheng Si, Libo Qin, Wenpeng Lu

Challenge:	Existing methods focus on local optimal while ignoring sole-mention disambiguation boosted by richer context from other mentions’ disambiguating processes.
Approach:	They propose an approach to extracting medical entity disambiguation using memory mechanism and memorized entity information (M3E) they use a memory mechanism module that performs memory caching, retrieval, fusion and cross-network residual to aid the disambiguations of remaining mentions.
Outcome:	The proposed method outperforms state-of-the-art methods on two benchmark datasets.

WSDPO: A Generative Word Sense Disambiguation Framework with Chain-of-Thought and Preference Optimization (2026.acl-long)

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Kunpeng Kang, Shuaimin Li, Kaiyuan Zhang, Luyang Zhang, Jiasheng Si, Bing Xu, Kehai Chen, Muyun Yang, Wenpeng Lu

Semantic Text Processing

Challenge:	Word sense disambiguation (WSD) is a fundamental task in natural language processing.
Approach:	They propose a training framework for generative WSD with chain-of-thought (CoT) and preference optimization.
Outcome:	The proposed framework achieves significant performance gains on rare and unseen settings and exhibits strong generalization in standard evaluation settings.

Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning (2026.acl-long)

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Ziqing Zhuang, Linhai Zhang, Jiasheng Si, Deyu Zhou, Yulan He

Reasoning

Challenge:	Existing approaches for improving LLM reasoning remain episodic and lack reusable meta-reasoning skills.
Approach:	They propose a framework that consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning.
Outcome:	The proposed framework consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning.