Papers by Shuangyin Li
DiffusPoll: Conditional Text Diffusion Model for Poll Generation (2024.findings-acl)
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| Challenge: | Social media platforms manipulate public opinion through sheer numbers and cause biases, authors say . they say new paradigm for poll generation can generate high-quality samples while preserving diversity . |
| Approach: | They propose a non-autoregressive diffusion model that uses masks to generate polls . they use attribute tags to enhance the quality of polls and to diversify poll options . |
| Outcome: | The proposed model matches the Transformer model while offering greater diversity and quality. |
R2AG: Incorporating Retrieval Information into Retrieval Augmented Generation (2024.findings-emnlp)
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| Challenge: | Existing approaches to augment large language models with external documents are lacking in the semantic gap between LLMs and retrievers due to differences in their training objectives and architectures. |
| Approach: | They propose to integrate R2AG into R2etrieval augmented generation framework by using a R2-Former to capture retrieval information. |
| Outcome: | The proposed framework fills the semantic gap between LLMs and retrievers due to differences in their training objectives and architectures. |
BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation (RAG) extends the capabilities of large language models (LLMs) by providing access to external knowledge. |
| Approach: | They propose a framework that emulates human interactive reading through annotation and re-reading by integrating a thought bubble module that offloads internal cognition into external bookmark tokens, which are then annotated back into the context. |
| Outcome: | The proposed framework offloads internal cognition into external bookmark tokens, which are then annotated back into the context. |
DiFRa: A Unified Framework for Harmonizing Semantic Diversity and Factual Consistency in Question-Answer Generation (2026.findings-acl)
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| Challenge: | Question-Answer Generation (QAG) is essential for domain-specific large language models post-training. |
| Approach: | They propose a framework that balances semantic diversity and factual consistency . they propose entropy and consistency scores that harmonize the trade-off between diversity and correctness . |
| Outcome: | The proposed framework outperforms baseline models in generating diverse QA pairs . the proposed framework harmonizes semantic entropy and consistency scores to quantify trade-off between diversity and correctness. |
RUBY: An Effective Framework for Multi-Constraint Multi-Hop Question Generation (2025.acl-long)
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| Challenge: | Existing frameworks for multi-hop question generation lack high-dimensional semantic constraints. |
| Approach: | They propose a framework which incorporates constraints into every hop of a multi-hop question . they propose dimensional constraint dimension reduction and divide-and-conquer-based dynamic projection . |
| Outcome: | The proposed framework outperforms baseline models in capturing and integrating constraints . it reduces randomness with added constraints, leading to more focused questions and fewer hop errors . |
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning (D19-1)
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| Challenge: | Existing methods for learning knowledge Graphs are incomplete and therefore need well-pretraining. |
| Approach: | They propose a deep reinforcement learning based model which incorporates LSTM and Graph Attention Mechanism as the memory components. |
| Outcome: | The proposed model can get rid of the pretraining process and achieve state-of-the-art performance compared with the other models. |
ToneCraft: Cantonese Lyrics Generation with Harmony of Tones and Pitches (2025.emnlp-main)
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| Challenge: | Current research has not addressed the challenge of generating harmonious Cantonese lyrics. |
| Approach: | They propose a framework for generating Cantonese lyrics that ensures tonal and melodic harmony. |
| Outcome: | The proposed framework ensures tonal and melodic harmony while preserving character count and quality. |
Topic-DPR: Topic-based Prompts for Dense Passage Retrieval (2023.findings-emnlp)
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| Challenge: | Prior research focused on optimizing a single prompt as a continuous prompt, but this approach leads to a semantic space collapse, preventing differentiation between relevant and irrelevant passages. |
| Approach: | They propose a dense passage retrieval model that uses topic-based prompts and propose 'positive and negative sampling strategies' to boost dense retrieval efficiency. |
| Outcome: | The proposed model surpasses state-of-the-art retrieval techniques and improves space uniformity. |