Papers by Haiqin Yang
A Unified One-Step Solution for Aspect Sentiment Quad Prediction (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing ASQP datasets are small and low-density, hindering technical advancement . et al. (2017): aspect sentiment quad prediction provides a complete aspect-level sentiment structure. |
| Approach: | They propose a one-step solution for Aspect sentiment quad prediction that can detect aspect categories and identify aspectopinion-sentiment triplets simultaneously. |
| Outcome: | The proposed solution can detect aspect categories and identify aspectopinion-sentiment triplets simultaneously. |
Intrinsic Test of Unlearning Using Parametric Knowledge Traces (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for “unlearning” information captured in large language models rely on behavioral tests without monitoring residual knowledge in model parameters. |
| Approach: | They propose a general evaluation methodology that uses vocabulary projections to inspect concepts encoded in model parameters. |
| Outcome: | The proposed method detects changes in parametric traces of unlearned concepts and localizes them in two open-source LLMs. |
A Two-Agent Game for Zero-shot Relation Triplet Extraction (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for relation triplet extraction rely on labeled data and are limited in their applicability. |
| Approach: | They propose a two-agent game approach to deliberate and debate unseen relations by two agents, a generator and an extractor. |
| Outcome: | The proposed method outperforms baseline methods by 6%-16% in F1 scores. |
A Diffusion Model for Event Skeleton Generation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for event schema generation are noise-sensitive and error-accumulating, e.g., inability to correct errors while generating schema. |
| Approach: | They propose a novel diffusion event graph model that embeds and roundes event graphs into learnable latent representations and a denoising process to maintain the model's robustness. |
| Outcome: | The proposed model achieves better results than existing state-of-the-art models on three IED bombing datasets. |
Task-wrapped Continual Learning in Task-Oriented Dialogue Systems (2025.findings-naacl)
Copied to clipboard
| Challenge: | Continual learning is vital for task-oriented dialogue systems (ToDs), but its performance is limited by training separate adapters for each task, preventing global knowledge sharing. |
| Approach: | They propose a framework that employs task-wrapped Adapters to learn global and task-specific information through parameter sharing. |
| Outcome: | The proposed framework outperforms AdapterCL in 37 domains while using only 46% of the parameters. |
Sparse Adapter Fusion for Continual Learning in NLP (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing methods for learning continuously face challenges such as inefficient parameter reuse across tasks and catastrophic forgetting when tasks are dissimilar. |
| Approach: | They propose a Sparse Adapter Fusion Method which dynamically fuses old and new adapters to address these challenges. |
| Outcome: | The proposed method outperforms state-of-the-art methods while utilizing less than 60% of the parameters. |
Let’s Play Across Cultures: A Large Multilingual, Multicultural Benchmark for Assessing Language Models’ Understanding of Sports (2025.emnlp-main)
Copied to clipboard
Punit Kumar Singh, Nishant Kumar, Akash Ghosh, Kunal Pasad, Khushi Soni, Manisha Jaishwal, Sriparna Saha, Syukron Abu Ishaq Alfarozi, Asres Temam Abagissa, Kitsuchart Pasupa, Haiqin Yang, Jose G Moreno
| Challenge: | Language Models (LMs) are primarily evaluated on globally popular sports, often overlooking regional and indigenous sporting traditions. |
| Approach: | They propose to use multiple-choice questions (MCQs) to assess LMs' understanding of traditional sports across 60 countries and 6 continents. |
| Outcome: | The new benchmark will be publicly available, fostering research in culturally aware AI systems. |
Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality (2026.findings-acl)
Copied to clipboard
Qipeng Xie, Zi Liang, Jiafei Wu, Yufei Chen, Weizheng Wang, Wenao Ma, Zhong Ming, Haiqin Yang, Kaishun Wu
| Challenge: | Existing studies on prompt engineering have focused on optimizing models for performance under stylistic perturbations. |
| Approach: | They conduct the first analysis of n-gram token-level mechanisms . they find that higher average performance is inherently associated with lower variance and greater stability. |
| Outcome: | The proposed model reduces the variance of the generated code by 40% . the proposed model is based on a large-scale dataset of 132,000 prompt variants . |
ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL (2026.acl-long)
Copied to clipboard
Yaxun Dai, Wenxuan Xie, Xialie Zhuang, Tianyu Yang, Ziyi Liu, Haiqin Yang, Yiying Yang, Yuhang Zhao, Pingfu Chao, Wenhao Jiang
| Challenge: | Current Text-to-SQL reasoning models lack integrated execution feedback during generation. |
| Approach: | They propose a text-to-SQL framework that interacts with the SQL execution engine during decoding and dynamically adjusts reasoning based on execution feedback. |
| Outcome: | The proposed framework achieves 89.1% accuracy on Spider and 65.3% on BIRD at the 7B scale. |
Dissecting Fine-Tuning Unlearning in Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning-based unlearning are ineffective at completely erasing model-embedded knowledge, but their true effectiveness remains unclear. |
| Approach: | They propose to use activation patching and parameter restoration experiments to examine the limitations of fine-tuning-based unlearning methods for erasing harmful, sensitive, or copyrighted information within large language models. |
| Outcome: | The proposed methods alter the model’s knowledge retrieval process rather than genuinely erasing the problematic knowledge embedded in the model parameters. |
PARSQL: Enhancing Text-to-SQL through SQL Parsing and Reasoning (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models have made significant strides in text-to-SQL tasks, but small language models struggle to accurately interpret natural language questions due to resource limitations. |
| Approach: | They propose a SQL parser that extracts constraints from SQL to generate sub-SQLs . they use a rule-based and LLM-based method to generate step-by-step SQL explanations based on the results . |
| Outcome: | The proposed framework outperforms models with the same model size on BIRD and Spider benchmarks. |
HiGRU: Hierarchical Gated Recurrent Units for Utterance-Level Emotion Recognition (N19-1)
Copied to clipboard
| Challenge: | Using textual features, our proposed HiGRU models achieve at least 8.7%, 7.5%, 6.0% improvement over the state-of-the-art methods on each dataset. |
| Approach: | They propose a hierarchical gated recurrent unit framework to model word-level inputs and an upper-level GRU to capture contexts of utterance-level embeddings. |
| Outcome: | The proposed framework achieves 8.7%, 7.5%, 6.0% improvement over state-of-the-art methods on three datasets. |