Papers by Liping Liu
Noisy Multi-Label Text Classification via Instance-Label Pair Correction (2024.findings-naacl)
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| Challenge: | Noise is a significant challenge for machine learning models, especially deep learning models. |
| Approach: | They propose a holistic selection metric that identifies noisy pairs while considering global loss information and instance-specific ranking information. |
| Outcome: | The proposed approach significantly improves performance in noisy multi-label text classification tasks. |
Mitigating Over-Generation for Unsupervised Keyphrase Extraction with Heterogeneous Centrality Detection (2023.emnlp-main)
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| Challenge: | Existing keyphrase extraction models incorrectly determine a keyphrase as a phrase but output other candidates as keyphrases because they contain the same word. |
| Approach: | They propose a new approach that detects both implicit and explicit centrality within a heterogeneous graph as the importance score of each candidate keyphrase. |
| Outcome: | The proposed approach outperforms state-of-the-art keyphrase extraction models on three benchmark datasets. |
EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models (2025.acl-long)
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| Challenge: | Existing methods for accelerating Large Vision-Language Models lack comprehensive evaluation across diverse backbones, benchmarks, and metrics. |
| Approach: | They propose EffiVLM-BENCH framework for evaluating absolute performance and generalization and loyalty. |
| Outcome: | The proposed framework offers insights into optimal strategies for accelerating LVLMs. |
StereoRel: Relational Triple Extraction from a Stereoscopic Perspective (2021.acl-long)
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| Challenge: | Existing methods for relational triple extraction still face challenges, including information loss and error propagation. |
| Approach: | They propose a model which maps relational triples to a three-dimensional space and leverages three decoders to extract them. |
| Outcome: | The proposed model outperforms the baselines on five public datasets. |
Instruct-of-Reflection: Enhancing Large Language Models Iterative Reflection Capabilities via Dynamic-Meta Instruction (2025.naacl-long)
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| Challenge: | Existing approaches involve models iterating and improving their previous responses based on internal reflection ability or external feedback. |
| Approach: | They propose a reflection framework that leverages meta-thoughts and self-consistency to enhance the iterative reflection capability of Large LanguageModels. |
| Outcome: | The proposed framework achieves an average improvement of 10.1% over established baselines in mathematical and commonsense reasoning tasks, highlighting its efficacy and applicability. |
SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)
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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin
| Challenge: | Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation. |
| Approach: | They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model. |
| Outcome: | The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks. |
Unsupervised Keyphrase Extraction by Learning Neural Keyphrase Set Function (2023.findings-acl)
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| Challenge: | Unsupervised keyphrase extraction is a task of extracting a keyphrase set that provides readers with highlevel information about the key ideas or important topics described in the document. |
| Approach: | They propose an unsupervised keyphrase extraction task that is a document-set matching problem instead of modeling the relevance between an individual phrase and the document. |
| Outcome: | The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction baselines by a large margin. |
HyperRank: Hyperbolic Ranking Model for Unsupervised Keyphrase Extraction (2023.emnlp-main)
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| Challenge: | Existing unsupervised keyphrase extraction models overlook latent hierarchical structures when extracting keyphrases. |
| Approach: | They propose a new ranking model that models global and local contexts to estimate the importance of each candidate keyphrase within the hyperbolic space. |
| Outcome: | The proposed model outperforms state-of-the-art models in keyphrase extraction tasks. |
Improving Embedding-based Unsupervised Keyphrase Extraction by Incorporating Structural Information (2023.findings-acl)
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| Challenge: | Existing unsupervised keyphrase extraction models ignore the indicative role of the highlights in certain locations, leading to wrong keyphrases extraction. |
| Approach: | They propose a Highlight-Guided Unsupervised Keyphrase Extraction model that models phrase-document relevance via the highlights of documents and calculates cross-phrase relevance between all candidate phrases. |
| Outcome: | The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction models on three benchmarks. |
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information (2025.coling-main)
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Yuxin Wang, MingHua Ma, Zekun Wang, Jingchang Chen, Shan Liping, Qing Yang, Dongliang Xu, Ming Liu, Bing Qin
| Challenge: | Existing LLM pruning works focus on unstructured pruning, which typically requires special hardware support for a practical speed-up. |
| Approach: | They propose a network pruning framework that leverages both coarse and fine-grained activation information as an importance criterion to guide pruning. |
| Outcome: | The proposed framework outperforms existing pruning methods on diverse models across sparsity budgets. |