Papers by Qihuang Zhong
Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models (2023.emnlp-main)
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| Challenge: | Existing zero-shot quantization methods are based on overfitting problem in adversarial learning process, leading to sub-optimal performance. |
| Approach: | They propose a zero-shot sharpness-aware quantization framework for the quantization of various PLMs by optimizing a minimax problem. |
| Outcome: | The proposed framework can achieve significant performance gains on discriminative and generative PLMs. |
Revisiting Knowledge Distillation for Autoregressive Language Models (2024.acl-long)
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| Challenge: | Autoregressive language models (LMs) are expensive and memory intensive, preventing the development of industrial applications. |
| Approach: | They propose an adaptive teaching approach to improve the KD of autoregressive language models by distilling knowledge into a small student model. |
| Outcome: | The proposed method can achieve consistent and significant performance gains across all model types and sizes. |
Revisiting Token Dropping Strategy in Efficient BERT Pretraining (2023.acl-long)
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| Challenge: | Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT. |
| Approach: | They propose a semantic-consistent learning method to improve token dropping by skipping the computation of a subset of input tokens at several middle layers. |
| Outcome: | The proposed method achieves consistent and significant performance gains across all tasks and model sizes. |
Token-Level Self-Evolution Training for Sequence-to-Sequence Learning (2023.acl-short)
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| Challenge: | Adaptive training approaches do not consider the variation of learning difficulty in different training steps, making the learning deterministic and sub-optimal. |
| Approach: | They propose a dynamic token-level self-evolution training method that reweighs the training losses of different target tokens based on priors. |
| Outcome: | Empirically, the proposed method yields significant improvements on three translation tasks. |
KaFT: Knowledge-aware Fine-tuning for Boosting LLMs’ Domain-specific Question-Answering Performance (2025.findings-acl)
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| Challenge: | Recent literature reveals that supervised fine-tuning (SFT) is suboptimal for domain-specific question-answering tasks. |
| Approach: | They propose a query diversification strategy for robust conflict detection and a knowledge-aware fine-tuning approach to effectively boost LLMs’ performance. |
| Outcome: | The proposed approach improves the model generalization and alleviates the hallucination. |
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-Based Sentiment Analysis (2022.coling-1)
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| Challenge: | Aspect-based sentiment analysis is sensitive to multi-aspect challenges, resulting in multiple aspects in a sentence. |
| Approach: | They propose a framework that leverages an in-domain generator to construct more multi-aspect samples . they then boost the robustness of ABSA models via contrastive learning on these generated samples ." |
| Outcome: | The proposed framework outperforms baselines without any augmentations on accuracy and Macro- F1 . the proposed framework can generate more multi-aspect samples and boost the robustness of ABSA models . |
Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks (2023.emnlp-main)
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Haoqi Zheng, Qihuang Zhong, Liang Ding, Zhiliang Tian, Xin Niu, Changjian Wang, Dongsheng Li, Dacheng Tao
| Challenge: | Text classification tasks often encounter few-shot scenarios with limited labeled data, and addressing data scarcity is crucial. |
| Approach: | They propose a self-evolution learning (SE) based mixup approach for data augmentation in text classification which generates more adaptive and model-friendly pseudo samples for the model training. |
| Outcome: | The proposed approach can generate more adaptive and model-friendly pseudo samples for the model training. |
ROSE Doesn’t Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding (2024.findings-acl)
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| Challenge: | Existing methods for aligning LLMs output with expected safety require substantial training efforts and expensive computational resources. |
| Approach: | They propose a method to directly boost the safety of existing instruction-tuned large language models without additional training. |
| Outcome: | The proposed method improves safety of instruction-tuned large language models without training and requires expensive computational resources. |
Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL (2024.findings-emnlp)
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| Challenge: | Existing text-to-SQL LLMs are computationally expensive and difficult to deploy in real-world applications. |
| Approach: | They propose to distill a larger teacher model into a smaller student model by using imperfect data to improve the KD. |
| Outcome: | The proposed method achieves the best tradeoff between performance and efficiency on 5 text-to-SQL benchmarks. |
Self-Evolution Learning for Discriminative Language Model Pretraining (2023.findings-acl)
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| Challenge: | Random masking does not consider the importance of the different words in the sentence meaning, e.g., entity-level masking requires expensive prior knowledge and generally does not use existing model weights. |
| Approach: | They propose a token masking and learning method that uses a random masking strategy to learn the under-explored tokens. |
| Outcome: | The proposed method improves linguistic knowledge learning and generalization on 10 tasks. |
Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models (2022.findings-emnlp)
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| Challenge: | Existing methods for fine-tuning pretrained language models suffer from poor generalization . however, they add a perturbation to each model parameter equally, which is sub-optimal . |
| Approach: | They propose a sharpness-aware minimization optimization procedure that introduces a Fisher mask to improve the efficiency of SAM. |
| Outcome: | The proposed method outperforms the vanilla sharpness-aware minimization method on GLUE and SuperGLUE benchmarks. |
Towards Making the Most of ChatGPT for Machine Translation (2023.findings-emnlp)
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| Challenge: | Prior studies have shown that ChatGPT achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resourced and distant-language-pairs translation. |
| Approach: | They propose task-specific prompts and domain-specific prompts which are based on task information and domain information and a task-specific prompt. |
| Outcome: | The proposed prompts improve the performance of ChatGPT in complex tasks and generate hallucinations for non-English-centric tasks. |