Papers by Tiankai Yang
AD-LLM: Benchmarking Large Language Models for Anomaly Detection (2025.findings-acl)
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Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, Yue Zhao
| Challenge: | Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. |
| Approach: | They propose a benchmark that evaluates how large language models (LLMs) can help with NLP anomaly detection. |
| Outcome: | The proposed model can perform zero-shot detection without tasks-specific training, data augmentation and model selection, and it can suggest unsupervised AD models. |
NLP-ADBench: NLP Anomaly Detection Benchmark (2025.findings-emnlp)
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| Challenge: | Anomaly detection (AD) is an important machine learning task, but its effectiveness in detecting harmful content, phishing attempts, and spam reviews is limited. |
| Approach: | They introduce NLP-ADBench, the most comprehensive NLP anomaly detection benchmark to date . it includes eight curated datasets and 19 state-of-the-art algorithms . |
| Outcome: | The NLP-ADBench benchmark includes 19 state-of-the-art methods and 8 curated datasets . no single model dominates across all datasets, indicating need for automated model selection . |
Treble Counterfactual VLMs: A Causal Approach to Hallucination (2025.findings-emnlp)
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| Challenge: | Existing studies link hallucination to data or representation biases, but their causal origins remain unclear. |
| Approach: | They propose a causal framework to analyze and mitigate hallucination in vision-language models by using counterfactual analysis to estimate the Natural Direct Effect (NDE) of each modality and their interaction. |
| Outcome: | The proposed framework significantly reduces hallucination while preserving task performance while retaining reliability. |
CoAct: Co-Active LLM Preference Learning with Human-AI Synergy (2026.acl-long)
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| Challenge: | Existing methods to learn from preference-based feedback are expensive and scarce. |
| Approach: | They propose a framework that synergistically combines self-rewarding and active learning through human-AI collaboration. |
| Outcome: | The proposed framework outperforms existing methods on three reasoning benchmarks and achieves average improvements of +13.25% on GSM8K, +8.19% on MATH, and +13.16% on WebInstruct. |