Papers by Lingqiao Liu
Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification (2021.findings-acl)
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| Challenge: | Existing studies focus on building a meta-learner from input text but ignore abundant semantic information beneath class labels. |
| Approach: | They propose a framework to make full use of label semantics in few-shot text classification systems. |
| Outcome: | The proposed framework can be plugged into the existing few-shot text classification system. |
I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated Responses (2024.emnlp-main)
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| Challenge: | Recent research has demonstrated that a large language model (LLM) can generate training data for another LLM, or for creating supplementary training materials, such as rationales. |
| Approach: | They conduct an in-depth investigation to understand why fine-tuning an LLM with responses generated by a LLM often yields better results than using responses generated from humans. |
| Outcome: | The proposed approach can be used to transfer knowledge from a larger model to a smaller one, or for creating supplementary training materials, such as rationales. |
Progressive Class Semantic Matching for Semi-supervised Text Classification (2022.naacl-main)
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| Challenge: | Recent semi-supervised learning methods have achieved impressive performance . semi-controlled learning can be used to reduce the annotation cost of text classifiers . |
| Approach: | They propose a semi-supervised learning process that builds a standard K-way classifier and a matching network for the input text and the Class Semantic Representation (CSR). |
| Outcome: | The proposed method improves baselines and overall is more stable. |
Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future (2023.emnlp-main)
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Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang
| Challenge: | Existing literature on the generalization of machine learning models to out-of-distribution data is lacking. |
| Approach: | They propose to present the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding. |
| Outcome: | The proposed survey provides the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding. |
Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems (2021.emnlp-main)
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| Challenge: | Existing approaches to integrate knowledge bases into end-to-end task-oriented dialogue systems are limited in their ability to properly represent the entity of KB. |
| Approach: | They propose a framework that dynamically perceives all relevant entities and dialogue history . it uses a Memory Mask to enforce the entity to focus on its relevant entities . |
| Outcome: | The proposed framework can achieve superior performance over the state of the arts. |
Semantic Role Labeling Guided Out-of-distribution Detection (2024.lrec-main)
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Jinan Zou, Maihao Guo, Yu Tian, Yuhao Lin, Haiyao Cao, Lingqiao Liu, Ehsan Abbasnejad, Javen Qinfeng Shi
| Challenge: | Existing methods for identifying domain-shifted instances are prone to OOD and adversarial inputs. |
| Approach: | They propose an unsupervised method that separates, extracts, and learns the semantic role labeling guided out-of-distribution Detection (SRLOOD) they propose a self-supervised approach to enhance global-local feature learning by predicting SRL extracted role. |
| Outcome: | The proposed method achieves SOTA performance on four OOD benchmarks. |
Efficiently Selecting Response Generation Strategies for Synthetic Data Construction by Self-Aligned Perplexity (2025.findings-emnlp)
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| Challenge: | Using a small sample of data, we find that perplexity is suboptimal in characterizing “familiarity” . |
| Approach: | They propose a method that assesses a small subset of generated data to estimate suitability for a specific target LLM. |
| Outcome: | The proposed method assesses a small subset of generated data to estimate suitability for a specific target LLM. |