Papers by Mingqi Li
Cross-media User Profiling with Joint Textual and Social User Embedding (C18-1)
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| Challenge: | Empirical studies demonstrate the effectiveness of the proposed approach to cross-media user profiling tasks. |
| Approach: | They propose a uniform user embedding learning approach to address cross-media user profiling by bridging the knowledge between the source and target media. |
| Outcome: | Empirical results show that the proposed approach performs well on two cross-media user profiling tasks. |
Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability (2024.emnlp-main)
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| Challenge: | Existing methods for evaluation of natural language generation tasks lack reliable data. |
| Approach: | They propose to use annotations from human and GPT-4 to construct a corpus for NLG evaluation. |
| Outcome: | The proposed corpus can perform flexible and interpretable evaluations without references and surpasses existing models. |
Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model (2022.emnlp-main)
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| Challenge: | Existing methods for improving multilingual models did not focus on learning the semantic structure of representation. |
| Approach: | They propose a method to improve multilingual language models by aligning parallel sentences . they propose token-, word-, sentence- and structure-level alignment objectives . |
| Outcome: | The proposed method outperforms baseline models on XNLI, PAWS-X, and XQuAD . it obtains comparable performance on low-resource languages, the authors show . |
Learning from Contrastive Prompts: An Automated Prompt Optimization Framework (2026.findings-acl)
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| Challenge: | Existing prompt optimization methods often underperform due to learning exclusively from incorrect samples. |
| Approach: | They propose a framework that leverages contrastive prompts to distinguish between high- and low-performing cases. |
| Outcome: | The proposed framework can generalize across open and proprietary models and NLU benchmarks. |
A Dual-Perspective NLG Meta-Evaluation Framework with Automatic Benchmark and Better Interpretability (2025.acl-long)
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| Challenge: | Existing evaluation metrics are insufficient to meet requirements for natural language generation. |
| Approach: | They propose a dual-perspective NLG meta-evaluation framework that focuses on different evaluation capabilities and a method of automatically constructing benchmarks without requiring new human annotations. |
| Outcome: | The proposed framework improves interpretability and provides better performance for 16 representative LLMs. |
Minos: A Multimodal Evaluation Model for Bidirectional Generation Between Image and Text (2026.findings-acl)
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| Challenge: | Existing evaluation models struggle to achieve consistent performance across image-to-text (I2T) and text-to image (T2I) tasks. |
| Approach: | They construct a multimodal evaluation model using a large multimodal dataset and rigorous quality control strategies to train it. |
| Outcome: | The proposed model achieves state-of-the-art evaluation performance across 16 out-of domain datasets covering both I2T and T2I tasks among all open-source multimodal evaluation models and remain competitive with closed-source models. |
Analyzing and Evaluating Correlation Measures in NLG Meta-Evaluation (2025.naacl-long)
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| Challenge: | Existing studies have not investigated the differences between different correlation measures in meta-evaluation. |
| Approach: | They analyze 12 common correlation measures using real-world data from six widely-used NLG evaluation datasets and 32 evaluation metrics. |
| Outcome: | The proposed measures exhibit the best performance in discriminative power and ranking consistency . the measures using system-level grouping or Kendall correlation are the least sensitive to score granularity . |