Papers by Hong Kang
Low-Resource Comparative Opinion Quintuple Extraction by Data Augmentation with Prompting (2023.findings-emnlp)
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| Challenge: | Comparative Opinion Quintuple Extraction (COQE) aims to predict comparative opinion quintuples from comparative sentences. |
| Approach: | They propose a low-resource approach to extract comparative opinion quintuples from comparative sentences . they propose augmentation using ChatGPT and a data-centric approach . |
| Outcome: | The proposed approach improves the existing pipeline-based method and achieves state-of-the-art results. |
Exploiting Numerical-Contextual Knowledge to Improve Numerical Reasoning in Question Answering (2022.findings-naacl)
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| Challenge: | Existing numerical reasoning models overly rely on parametric knowledge at inference time . previous studies show that understanding numbers in text improves numerical reasoning accuracy . |
| Approach: | They propose a numerical reasoning model that leverages parametric knowledge to alleviate this over-reliance on parametric information. |
| Outcome: | The proposed model improves numerical reasoning accuracy and performance in DROP. |
Handling Anomalies of Synthetic Questions in Unsupervised Question Answering (2020.coling-main)
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| Challenge: | Existing approaches to improve unsupervised Question Answering (UQA) are expensive and require additional datasets. |
| Approach: | They propose an unsupervised QA approach that generates QA training data automatically. |
| Outcome: | The proposed method improves unsupervised QA significantly across a number of QA tasks. |
Have You Seen That Number? Investigating Extrapolation in Question Answering Models (2021.emnlp-main)
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| Challenge: | Numerical reasoning in machine reading comprehension (MRC) has shown drastic improvements over the past few years. |
| Approach: | They propose an E-digit number form that alleviates the lack of extrapolation in numerical MRC models. |
| Outcome: | The proposed model can't extrapolate to unseen numbers, the authors say . they also show that the model needs to treat numbers differently from regular words . |
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise (2024.findings-naacl)
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| Challenge: | Existing retrieval-augmented language models assume query relevance and irrelevance as dichotomy . existing models are highly brittle to the presence of conflicting information in both the fine-tuning and in-context few-shot learning scenarios. |
| Approach: | They propose methods for handling knowledge conflicts by fine-tuning a discriminator or prompting it to elicit its discriminative capability. |
| Outcome: | The proposed approaches significantly enhance model robustness on open-domain QA. |
Regularization of Distinct Strategies for Unsupervised Question Generation (2020.findings-emnlp)
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| Challenge: | Unsupervised question answering (UQA) is a task of answering questions from a context that contains the answer. |
| Approach: | They propose a method to generate higher-quality questions with a teacher-student architecture and a regularization module to avoid bias toward a particular question generation strategy. |
| Outcome: | The proposed method generates higher-quality questions across diverse datasets and tasks and can be used to create a model with few-shot learning. |
QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs (2026.findings-acl)
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| Challenge: | Recent studies have shown that large language models (LLMs) can be effective for correcting factual inaccuracies but can still suffer from hallucinations. |
| Approach: | They propose a queue-based self-correction framework that addresses parameter bias during sequential model editing. |
| Outcome: | The proposed framework outperforms baseline models while maintaining competitive performance in single-turn editing. |
Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval (2021.emnlp-main)
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| Challenge: | Recent approaches to information retrieval (IR) and natural language processing (NLP) use contextual language models, which can improve both synonymy and polysemy problems associated with words. |
| Approach: | They propose an ultra-high dimensional representation scheme equipped with directly controllable sparsity and a bucketing method where embeddings from multiple layers of BERT are selected/merged to represent diverse linguistic aspects. |
| Outcome: | The proposed representation scheme outperforms sparse models with MS MARCO and TREC CAR, and shows that it is highly efficient for storage and search. |
I Don’t Need Solution. I Need Emotional Support : Empathetic LLMs based on Emotional Validation (2026.findings-acl)
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| Challenge: | Existing large language models (LLMs) struggle to generate emotional support response, despite observing and reflecting on the help-seeker’s situation . Empathy drives the formation of constructive interpersonal and supportive relationships, including counseling for mental health care . |
| Approach: | They propose to use a two-stage training process to enhance empathetic response generation through empathy acquisition and emotional validation alignment. |
| Outcome: | The proposed method significantly improves empathetic response generation, achieving superior performance in both automatic and human evaluations. |
Scaling Unverifiable Rewards: A Case Study on Visual Insights (2026.findings-acl)
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| Challenge: | Existing methods to scale complex, open-ended tasks with unverifiable rewards are not scalable to multi-stage pipelines. |
| Approach: | They propose a process-based refinement framework that scales inference across stages of a multi-agent pipeline, instead of refining a single output over time. |
| Outcome: | The proposed framework scales inference across stages of a multi-agent pipeline, instead of refining a single output over time as in prior work. |
Graph-Induced Transformers for Efficient Multi-Hop Question Answering (2022.emnlp-main)
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| Challenge: | Recent MHQA tasks that require inter-paragraph/sentence linkages use graphs to model internal structural information within text. |
| Approach: | They propose a graph-induced transformer that applies graph-derived attention patterns directly into a PLM without external graph modules. |
| Outcome: | The proposed model can replace external graph modules while preserving model performance. |
COF: Adaptive Chain of Feedback for Comparative Opinion Quintuple Extraction (2025.coling-main)
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| Challenge: | Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text. |
| Approach: | They propose a model-unaware adaptive chain-of-feedback method to extract quintuples from product review text. |
| Outcome: | The proposed method improves performance on three benchmarks. |
Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization (2026.acl-long)
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Weixu Zhang, Ye Yuan, Changjiang Han, Yuxing Tian, Zipeng Sun, Linfeng Du, Jikun Kang, Hong Kang, Xue Liu, Haolun Wu
| Challenge: | Large Language Models exhibit strong implicit personalization ability, but most approaches treat this behavior as a black box. |
| Approach: | They propose a mechanistic interpretation perspective and propose 'sparse' set of Preference Heads . they compute a Preference Contribution Score for each attention head and compare their predictions . |
| Outcome: | The proposed framework computes a Preference Contribution Score (PCS) for each attention head and measures its causal impact on user aligned outputs. |