Papers by Qingyang Wu
Unsupervised Morphological Tree Tokenizer (2025.findings-acl)
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| Challenge: | Conventional statistical tokenizers often disrupt constituent boundaries within words, thereby corrupting semantic information. |
| Approach: | They propose a method that uses morphological structure guidance to induce character-level structures of words by training a deep model. |
| Outcome: | Empirical results show that the proposed method retains complete morphemes and outperforms existing methods on morphological segmentation and language modeling tasks. |
Reasoning or Knowledge: Stratified Evaluation of Biomedical LLMs (2026.eacl-long)
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| Challenge: | Medical reasoning in large language models is a complex cognitive process through which clinicians interpret patient data and make diagnostic and therapeutic decisions. |
| Approach: | They propose an evaluation framework that disentangles knowledge recall from reasoning by training a PubMedBERT-based classifier and applying it to 11 widely used biomedical QA benchmarks. |
| Outcome: | The proposed evaluation framework disentangles knowledge recall from reasoning by training a PubMedBERT-based classifier and applying it to 11 widely used biomedical QA benchmarks. |
kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning (2024.naacl-long)
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Wenting Zhao, Ye Liu, Yao Wan, Yibo Wang, Qingyang Wu, Zhongfen Deng, Jiangshu Du, Shuaiqi Liu, Yunlong Xu, Philip Yu
| Challenge: | Recent advances in task-oriented parsing involve formulating the task as a sequence-to-sequence problem, relying on a wealth of labeled data. |
| Approach: | They propose a task-oriented parsing framework that integrates nearest-neighbor learning with a nearest-nearest approach. |
| Outcome: | The proposed model can be used to synthesize computer programs based on a natural-language prompt without additional data or specialized prompts. |
PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation (2021.acl-short)
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| Challenge: | Existing approaches to building task-oriented dialog systems require a substantial amount of annotations and thus are labor-intensive. |
| Approach: | They propose a Pre-trainedRole Alternating Language model (PRAL) that is explicitly designed for task-oriented dialog tasks. |
| Outcome: | The proposed model outperforms or is on par with state-of-the-art models on task-oriented dialog tasks. |
KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning (2023.emnlp-main)
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| Challenge: | Task-oriented dialogs (TOD) require a model to generate a response that optimizes for task-related metrics. |
| Approach: | They propose a faster generation procedure that samples from independent next-word distributions and introduce a fine-grained reward function to help the model focus on learning key information in a dialog. |
| Outcome: | The proposed algorithm achieves state-of-the-art performance on an offline task with 15% training time reduction compared to a standard RL algorithm using auto-regressive generation. |
CausalEval: Towards Better Causal Reasoning in Language Models (2025.naacl-long)
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Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan
| Challenge: | Large language models (LLMs) have been used for a variety of tasks, including problem-solving, decision-making, and understanding of the world. |
| Approach: | They propose a review of existing methods aimed at enhancing LMs for causal reasoning . they categorize existing methods as reasoning engines or as helpers providing knowledge or data to traditional methods . |
| Outcome: | The proposed methods perform better than existing methods on a range of tasks. |
DECOR: Improving Coherence in L2 English Writing with a Novel Benchmark for Incoherence Detection, Reasoning, and Rewriting (2024.emnlp-main)
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| Challenge: | Existing automated writing evaluation systems only detect incoherence in writing . a recent study has found that incorporating specific reasons for incohence improves the quality of rewrites . |
| Approach: | They propose a benchmark that includes expert annotations for detecting incoherence in L2 English writing, identifying the underlying reasons, and rewriting the incoerent sentences. |
| Outcome: | The proposed benchmark improves coherence in L2 English writing by fine-tuning models . the authors find that incorporating specific reasons improves quality of rewrites . |
Stateful Memory-Augmented Transformers for Efficient Dialogue Modeling (2024.findings-eacl)
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| Challenge: | Existing Transformers models are computationally expensive for long context inputs. |
| Approach: | They propose a transformer that can interchange information between memory states and context . they evaluate the efficiency of their model on three dialogue datasets and two language datasets . |
| Outcome: | The proposed model is compatible with existing transformer models and can preserve dialogue history information. |
Memformer: A Memory-Augmented Transformer for Sequence Modeling (2022.findings-aacl)
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| Challenge: | Experimental results show that Memformer uses 8.1x less memory space and 3.2x faster on inference. |
| Approach: | They propose an efficient neural network that utilizes an external dynamic memory to encode and retrieve past information. |
| Outcome: | The proposed model achieves comparable performance against baselines with 8.1x less memory space and 3.2x faster on inference. |
LIONs: An Empirically Optimized Approach to Align Language Models (2024.emnlp-main)
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| Challenge: | Recent studies have focused on aligning large language models with pre-trained datasets. |
| Approach: | They conduct a rigorous analysis of a three-stage training pipeline using sequence packing, loss masking and increasing the preference dataset size in DPO to improve the performance of language models. |
| Outcome: | The proposed models outperform the official instruct models tuned with closed-source data and algorithms. |
On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)
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Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie
| Challenge: | under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors. |
| Approach: | They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients . |
| Outcome: | The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets. |
Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models (2021.eacl-main)
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| Challenge: | Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks. |
| Approach: | They propose a framework that uses pre-trained language to model each speaker separately . it can be generalized to more challenging, non-collaborative tasks such as persuasion . |
| Outcome: | The proposed framework outperforms or is on par with state-of-the-art methods on two popular datasets: CamRest676 and MultiWOZ. |
Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale (2024.acl-long)
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| Challenge: | Existing syntactic language models require a gold tree and sequential training to generate sentences. |
| Approach: | They propose an unsupervised syntactic language model that incrementally generates a sentence with its syntaktic tree in a left-to-right manner. |
| Outcome: | The proposed model outperforms existing models on grammar induction and comprehension tasks while holding a substantial acceleration on training. |