Papers by Mao Ye
VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models (2024.emnlp-main)
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| Challenge: | Recent research has focused on pushing weight-only quantization to extremely low-bit due to numerical representation limitations. |
| Approach: | They propose a vector-based quantization approach that pushes LLMs to extremely low-bit . they propose scalar-based weight quantization that reduces memory requirements and optimizes storage costs . |
| Outcome: | The proposed method reduces model quantization perplexity by 0.01-0.34 on LLaMA-2, 0.38-0.68 on mistral-7B, 4.41-7.34, on llaMA-3 on QA tasks on average. |
CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios (2025.emnlp-main)
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| Challenge: | a number of tools are used to perform complex tasks, but the tool utilization process can cause errors. |
| Approach: | They propose a critique evaluation benchmark for tool learning that analyzes function-calling errors on tool evaluation benchmarks. |
| Outcome: | The proposed critique evaluation benchmark holds diverse tool-use errors with varying complexities, which better reflects real-world scenarios. |
Read Before Grounding: Scene Knowledge Visual Grounding via Multi-step Parsing (2025.coling-main)
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| Challenge: | Existing VG datasets use simple textual descriptions with limited attribute and spatial information between images and text. |
| Approach: | They propose a method that transforms visual knowledge into concise, information-dense visual descriptions. |
| Outcome: | The proposed method significantly improves performance of multimodal grounding models. |
ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)
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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong
| Challenge: | Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs). |
| Approach: | They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models. |
| Outcome: | The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks. |
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions (2020.acl-main)
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| Challenge: | State-of-the-art NLP models can be fooled by human-unaware transformations such as synonymous word substitution. |
| Approach: | They propose a method that constructs a stochastic ensemble by applying random word substitutions on the input sentences and leverages the statistical properties to provably certify the robustness. |
| Outcome: | The proposed method outperforms state-of-the-art methods on IMDB and Amazon text classification tasks with practically meaningful certified accuracy. |
Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction (2024.findings-acl)
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| Challenge: | Chinese Spelling Correction (CSC) lacks large-scale high-quality corpora due to labor-intensive labeling of spelling errors in real-life writing or typing scenarios. |
| Approach: | They propose to use OCR/ASR-based generation to refine Chinese Spelling Correction models on random replacement-based corpora and filter them based on prediction confidence. |
| Outcome: | The proposed model outperforms existing models on three widely-used benchmarks while significantly alleviating over-correction. |
TREA: Tree-Structure Reasoning Schema for Conversational Recommendation (2023.acl-long)
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| Challenge: | Recent reasoning-based models cannot fully figure out complex causal relationships between mentioned entities with external knowledge. |
| Approach: | They propose a Tree structure Reasoning schEmA that constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities. |
| Outcome: | Extensive experiments on two public CRS datasets show the proposed model works. |