Papers by Fanyi Qu
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. |
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. |
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. |