Papers by Kezhi Mao
Closed Boundary Learning for Classification Tasks with the Universum Class (2023.findings-emnlp)
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| Challenge: | Existing methods treat the Universum class equally with the classes of interest, leading to problems such as overfitting, misclassification, and diminished model robustness. |
| Approach: | They propose a closed boundary learning method that applies closed decision boundaries to classes of interest and designates the area outside all closed boundaries as the Universum class. |
| Outcome: | The proposed method improves accuracy and robustness of classification models on six state-of-the-art tasks. |
Beyond the Next Token: Towards Prompt-Robust Zero-Shot Classification via Efficient Multi-Token Prediction (2025.naacl-long)
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| Challenge: | Existing methods for zero-shot text classification lack prompt engineering due to prompt brittleness . however, these methods are not effective for zero shot text classifications . |
| Approach: | They propose a method that predicts token probabilities across multiple positions and simulates comprehensive sampling of generation paths in a single run of a language model. |
| Outcome: | The proposed approach improves accuracy and reduces standard deviation by 98% . it maintains comparable performance even without a prompt, reducing the need for prompt engineering . |
FreeCtrl: Constructing Control Centers with Feedforward Layers for Learning-Free Controllable Text Generation (2024.acl-long)
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| Challenge: | Controllable text generation (CTG) focuses on crafting texts adhering to specific attributes . studies show learning-based methods require extensive computational and data resources . |
| Approach: | They propose a learning-free approach that dynamically adjusts the weights of selected feedforward neural network vectors to steer the outputs of large language models. |
| Outcome: | The proposed approach outperforms learning-based and learning-free methods on multi-attribute control. |
Document-Level Event Argument Extraction by Leveraging Redundant Information and Closed Boundary Loss (2022.naacl-main)
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| Challenge: | Document-level event argument extraction is a crucial subtask of event extraction. |
| Approach: | They propose to use redundant event information to extract multiple arguments from a document . they propose a loss function to classify Universum class by their open decision boundary . |
| Outcome: | The proposed model outperforms the previous state-of-the-art models by 3.35% in F1-score. |
Rethinking Prompt Optimizers: From Prompt Merits to Optimization (2026.eacl-long)
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Zixiao Zhu, Hanzhang Zhou, Zijian Feng, Tianjiao Li, Chua Jia Jim Deryl, Lee Onn Mak, Gee Wah Ng, Kezhi Mao
| Challenge: | Existing methods to optimize prompts rely on LLMs' self-generation ability but lack interpretability due to implicit optimization. |
| Approach: | They propose a model-agnostic prompt quality merits and a merit-guided, locally deployable prompt optimizer trained on a lightweight LLM to improve prompt quality. |
| Outcome: | The proposed model avoids online optimization, reduces privacy concerns, and generalizes effectively to both large-scale and lightweight inference models. |
Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context Learning (2025.naacl-long)
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| Challenge: | Effective organization of in-context learning (ICL) demonstrations is key to improving the quality of large language models (LLMs). |
| Approach: | They propose a logit separability-based method that integrates multiple class-related words into each sample-label pair to improve LLM understanding. |
| Outcome: | The proposed method improves ICL performance by providing clearer instructions and richer label information. |
EDEntail: An Entailment-based Few-shot Text Classification with Extensional Definition (2024.findings-naacl)
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| Challenge: | Existing methods for few-shot text classification use either class labels or intensional definitions of class labels for label semantics expression. |
| Approach: | They propose a method that employs extensional definition of class labels in hypotheses and then order and format them into a sequence to form hypothese . |
| Outcome: | The proposed method surpasses supervised-learning methods and prompt-based methods on five classification datasets and is comparable to state-of-the-art models. |
LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction (2024.acl-long)
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| Challenge: | In-context learning (ICL) is an emerging ability of large-scale labeled data for document-level event argument extraction (EAE). |
| Approach: | They propose an explicit heuristic-driven demonstration construction approach that emphasizes task heurs in document-level event argument extraction tasks. |
| Outcome: | The proposed method outperforms existing prompting methods and few-shot supervised learning methods on document-level EAE datasets. |
Improving Relation Extraction with Knowledge-attention (D19-1)
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| Challenge: | Existing attention mechanisms are data-driven, but most are data driven. |
| Approach: | They propose a knowledge-attention encoder which integrates prior knowledge from external lexical resources into deep neural networks for relation extraction task. |
| Outcome: | The proposed system outperforms existing CNN, RNN, and self-attention based models on a large-scale relation extraction dataset. |
PromptExplainer: Explaining Language Models through Prompt-based Learning (2024.findings-eacl)
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| Challenge: | Existing explanation methods rely on linear approximations, accentuating irrelevant input tokens. |
| Approach: | They propose a method that aligns the explanation process with the masked language modeling task of pretrained language models and leverages prompt-based learning to generate class-dependent explanations. |
| Outcome: | Extensive experiments show that PromptExplainer outperforms state-of-the-art explanation methods. |