Papers by Yangyi Li
Quantifying and Understanding Uncertainty in Large Reasoning Models (2026.acl-long)
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| Challenge: | Existing methods for estimating generation uncertainty do not provide finite-sample guarantees for reasoning-answer generation. |
| Approach: | They propose a method that provides the uncertainty of the reasoning-answer structure with statistical guarantees. |
| Outcome: | The proposed method disentangles reasoning quality from answer correctness while establishing theoretical guarantees for efficient explanation methods. |
ONION: A Simple and Effective Defense Against Textual Backdoor Attacks (2021.emnlp-main)
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| Challenge: | Backdoor attacks can manipulate the output of deep neural networks and possess high insidiousness. |
| Approach: | They propose a textual backdoor defense based on outlier word detection that can handle all the textual attacks. |
| Outcome: | The proposed method can handle all the textual backdoor attack situations. |
Quantifying Uncertainty in Natural Language Explanations of Large Language Models for Question Answering (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown strong capabilities, enabling concise, context-aware answers in question answering tasks. |
| Approach: | They propose a framework that provides valid uncertainty guarantees for LLMs . they also propose 'model-agnostic' uncertainty estimation method that maintains valid guarantees even under noise. |
| Outcome: | The proposed method provides valid uncertainty guarantees even under noise. |
Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger (2021.acl-long)
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| Challenge: | Existing methods for textual backdoor attacks insert additional contents into normal samples as triggers, causing detection and blocking of backdoors. |
| Approach: | They propose to use syntactic structure as trigger in textual backdoor attacks . they propose to achieve similar attack performance but have higher invisibility . |
| Outcome: | The proposed method achieves almost 100% success rate but has higher invisibility and stronger resistance to defenses than the insertion-based methods. |
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer (2021.emnlp-main)
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| Challenge: | Experimental results show that popular NLP models are vulnerable to both adversarial and backdoor attacks based on text style transfer. |
| Approach: | They propose to conduct adversarial and backdoor attacks based on text style transfer . the authors propose to use text style to alter the style of a sentence . |
| Outcome: | The proposed methods show that popular models are vulnerable to both attacks based on text style transfer . the results show that the proposed methods perform better than baselines in many aspects . |
ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representation (2023.emnlp-main)
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| Challenge: | State-of-the-art vision-language models have limited performance in structural knowledge extraction, such as relations between objects. |
| Approach: | They propose to leverage the inherent structure of programming language to depict visual structural information in a well-organized structured format. |
| Outcome: | The proposed framework improves visual structural knowledge extraction on visual structure prediction tasks. |