Papers by Fanyi Wu
Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation (2022.coling-1)
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| Challenge: | Existing question generation models treat input passage as a sequence-to-sequence generative task, but they are not aware of text structure. |
| Approach: | They propose to model text structure as answer position and syntactic dependency and propose a mask attention mechanism to make syntaktic structure of input passage accessible. |
| Outcome: | The proposed model outperforms the strong pre-trained model ProphetNet on a SQuAD dataset and achieves competitive results with the state-of-the-art model. |
Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction (2024.naacl-long)
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| Challenge: | In-context learning (ICL) has shown impressive results on many tasks, but applying LLMs to grammatical error correction (GEC) is still a challenging task. |
| Approach: | They propose an ungrammatical-syntax-based in-context example selection strategy that measures similarity of sentences based on their syntactic structures and identify optimal ICL examples sharing the most similar ill-formed syntax to the test input. |
| Outcome: | The proposed strategy outperforms word-matching and semantics-based methods on a syntax-oriented task like GEC on benchmark English datasets. |
Video-MMMU: Evaluating Knowledge Acquisition from Multidisciplinary Professional Videos (2026.acl-long)
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| Challenge: | Existing video benchmarks do not evaluate the knowledge acquisition capabilities of Large Multimodal Models (LMMs) existing video benchmark focuses on static, general visual understanding tasks, without evaluating whether models can acquire knowledge dynamically. |
| Approach: | They propose a multi-modal, multi-discipline, multitrack benchmark that evaluates Large Multimodal Models’ ability to acquire knowledge from college-level, educational videos. |
| Outcome: | The proposed benchmark reveals a substantial gap between human learners and current Large Multimodal Models (LMMs) and focuses on improving their learning efficiency. |
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding (2024.lrec-main)
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| Challenge: | Multimodal semantic understanding is crucial for developing machines capable of interpreting complex interplay of text and visual information. |
| Approach: | They propose a multi-modal soft prompt framework that integrates three experts of soft prompts . they propose sarcasm detection and sentiment analysis tasks that are critical for few-shot learning . |
| Outcome: | The proposed model outperforms the 8.2B model InstructBLIP with 2% parameters . it significantly outperformed other prompt methods on VLMs or task-specific methods . |
Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding (2024.findings-acl)
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| Challenge: | Recent studies show that large-scale models can generate unsupervised DG without expensive distractor annotations. |
| Approach: | They propose a dual task training framework that integrates pseudo distractors from LLMs and answer information as the objective target with a two-stage training process. |
| Outcome: | The proposed method surpasses GPT-3.5-turbo zero-shot performance with 200 fewer model parameters. |
THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction (2025.findings-emnlp)
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| Challenge: | Existing approaches to risk prediction from EHRs handle structured diagnostic codes and unstructured narrative notes separately. |
| Approach: | They propose a Temporal-Hierarchical Causal Model with Conformal Calibration . they construct a multimodal causal graph where nodes represent clinical entities from two modalities . |
| Outcome: | The proposed model infers three clinically grounded interactions from textual propositions and ICD codes mapped to textual descriptions. |
Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data (2021.emnlp-main)
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| Challenge: | Existing approaches to generate high quality question-answer pairs are limited . a new framework is proposed for the question-answer generation task on real-world examination data. |
| Approach: | They propose a multi-agent communication model to generate and optimize the question and keyphrases iteratively and then apply the generated question and keys to guide the generation of answers. |
| Outcome: | The proposed framework makes great breakthroughs in the question-answer pair generation task. |