🧑‍🔬 Yifei Zhang

🏛️ Affiliations

🎓Northeastern UniversityShenyang · China2019–2026 · 20 paperscurrent🏥Hebei General HospitalShijiazhuang · China2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. MoLAN: A Unified Modality-Aware Noise Dynamic Editing Framework for Multimodal Sentiment Analysis (2026 · 1 citation)
   Multimodal sentiment analysis (MSA) models often get misled by irrelevant visual or acoustic signals, such as background laughter that contradicts a speaker's mood.
2. NEAT: Neuron-Based Early Exit for Large Reasoning Models (2026 · 1 citation)
   Large Reasoning Models frequently keep producing reasoning after they have already reached a correct solution.
3. PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs (2026 · 1 citation)
   Multimodal instruction fine-tuning tends to weaken the text reasoning that MLLMs inherit from their base language models.
4. Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation (2026 · 1 citation)
   The paper tackles multimodal sentiment analysis of text-image posts when only a small model can be deployed.
5. CIRAG: Construction–Integration Retrieval and Adaptive Generation for Multi-hop Question Answering (2026)
   Triple-based iterative RAG for multi-hop QA has two weaknesses. It keeps only one best triple per step, so early mistakes compound and parallel evidence is lost. It also uses a single evidence granularity, which can be too noisy or too sparse depending on the question. CIRAG draws on Kintsch's…
6. GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing (2026)
   Psychological profiling of persona-conditioned LLM agents (PC-Agents) usually relies on self-report questionnaires such as BFI, BDI or MBTI.
7. Identifying the Achilles' Heel: An Iterative Method for Uncovering Factual Errors in Large Language Models (2026)
   Large language models often state false facts with confidence. Existing factuality benchmarks have four main weaknesses: they need costly manual annotation, their static test sets may have leaked into training data, they cover few topics or question formats, and they cannot steer testing toward a model's weak spots.…

All works

9 works of theirs in our library, the most cited first; 7 with a page here, the others link their original until we summarise them.

1. MoLAN: A Unified Modality-Aware Noise Dynamic Editing Framework for Multimodal Sentiment AnalysisFindings of the Association for Computational LinguisticsXingle Xu, YongKang Liu, Dexian Cai, et al.2026 · 1 citation
2. NEAT: Neuron-Based Early Exit for Large Reasoning ModelsFindings of the Association for Computational LinguisticsKang Liu, YongKang Liu, Xiaocui Yang, et al.2026 · 1 citation
3. PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMsFindings of the Association for Computational LinguisticsZijing Wang, YongKang Liu, Mingyang Wang, et al.2026 · 1 citation
4. Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and DistillationFindings of the Association for Computational LinguisticsHaonan Shangguan, Xiaocui Yang, Shi Feng, et al.2026 · 1 citation
5. CIRAG: Construction–Integration Retrieval and Adaptive Generation for Multi-hop Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL)Zili Wei, Yilin Wang, Xiaocui Yang, et al.2026
6. GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective TestingAnnual Meeting of the Association for Computational Linguistics (ACL)Ming Wang, Shuang Wu, Bixuan Wang, et al.2026
7. Identifying the Achilles' Heel: An Iterative Method for Uncovering Factual Errors in Large Language ModelsFindings of the Association for Computational LinguisticsWenxuan Wang, Yuk-Kit Chan, Zixuan Ling, et al.2026
8. RATION: Entropy-Driven Task-Adaptive Visual Attention Allocation Framework for Multimodal Reasoning ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetXingle Xu et al.2026
9. Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetYifei Zhang et al.2026

🤝 Co-authors here

12 of 25 co-authors with a page, the most papers together first.

• Daling Wang ×6
• Shi Feng ×6
• Xiaocui Yang ×6
• YongKang Liu ×3
• Ercong Nie
• Ge Yu
• Hinrich Schuetze
• Jen-tse Huang
• Kang Liu
• Lu Lin
• Michael R. Lyu
• Ming Wang

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics ×5: Findings of the Association for Computational Linguistics 2026 ×5
• Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026 ×2
• Findings of the Association for Computational Linguistics: ACL 2026 ×2

🔗 Cited by

Papers of our library cite their works 3 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated 1 November 2026

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