Papers by Mingyue Liu
Self-Regulated Sample Diversity in Large Language Models (2024.findings-naacl)
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Mingyue Liu, Jonathan Frawley, Sarah Wyer, Hubert P. H. Shum, Sara Uckelman, Sue Black, Chris Willcocks
| Challenge: | Existing methods that require expensive setups or maintain static values during inference are inflexible and require expensive training. |
| Approach: | They propose a self-regulating approach that adjusts sample diversity parameters dynamically based on the input prompt. |
| Outcome: | The proposed method significantly improves the quality of responses generically without model retraining or fine-tuning. |
TestAgent: An Adaptive and Intelligent Expert for Human Assessment (2025.findings-acl)
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| Challenge: | Existing adaptive testing methods face several challenges due to mechanized nature of most algorithms and noisy response data. |
| Approach: | They propose to use large language models to enhance adaptive testing through interactive engagement to capture test-takers’ responses and anomalies. |
| Outcome: | The proposed agent achieves more accurate results with 20% fewer questions than state-of-the-art baselines and testers preferred it in speed, smoothness, and other dimensions. |
CodeFort: Robust Training for Code Generation Models (2024.findings-emnlp)
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Yuhao Zhang, Shiqi Wang, Haifeng Qian, Zijian Wang, Mingyue Shang, Linbo Liu, Sanjay Krishna Gouda, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras
| Challenge: | Existing research efforts to improve code generation models are inadequate . code generation model performance is degraded under small perturbations . |
| Approach: | They propose a framework to improve the robustness of code generation models by generalizing code perturbations to enrich training data and enabling various robust training strategies. |
| Outcome: | The proposed framework increases pass rates and robustness drop rate against code-syntax perturbations. |
Entity Resolution in Open-domain Conversations (2021.naacl-industry)
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Mingyue Shang, Tong Wang, Mihail Eric, Jiangning Chen, Jiyang Wang, Matthew Welch, Tiantong Deng, Akshay Grewal, Han Wang, Yue Liu, Yang Liu, Dilek Hakkani-Tur
| Challenge: | Recent work on incorporating external knowledge into the response generation models has attracted great interest. |
| Approach: | They propose a neural entity linking approach to incorporate external knowledge into the response generation models to improve the relevancy of retrieved knowledge. |
| Outcome: | The proposed approach outperforms the baseline model by 62.8% relative to the baseline. |
HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation (2025.acl-long)
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| Challenge: | Current approaches to addressing knowledge outdating in LLMs struggle with retrieval and generation aspects when handling outdated information. |
| Approach: | They propose a benchmark to evaluate the impact of outdated information on RAG . they use token-level diff algorithms and LLM pipelines to create a large-scale QA dataset . |
| Outcome: | The proposed benchmark analyzes the impact of outdated information on RAG performance. |
Re3: Relevance & Recency Retrieval for Mitigating Temporal Hallucination (2026.acl-long)
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| Challenge: | Existing retrievers suffer from temporal-semantic misalignment and outdated-document interference . Existing frameworks suffer from both temporal validity and outdated factual versions . |
| Approach: | They propose a framework that mitigates temporal hallucinations by embedding heterogeneous temporal signals into the semantic space to ensure retrieval fidelity. |
| Outcome: | Experiments show that Re3 outperforms baselines by 9.7% in generation accuracy . the framework outperformed strongest baselines on challenging dynamic tasks . |