Papers by Qingyan Guo
LaDiC: Are Diffusion Models Really Inferior to Autoregressive Counterparts for Image-to-Text Generation? (2024.naacl-long)
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| Challenge: | Existing models for text-to-image generation have been underperforming in image-totext generation tasks. |
| Approach: | They propose a framework that uses a split BERT to create a dedicated latent space for captions and integrates a regularization module to manage varying text lengths. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the MS COCO dataset with 38.2 BLEU@4 and 126.2 CIDEr . |
Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints (2025.acl-short)
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| Challenge: | Semantic Parsing improves performance of smaller models, but it is unclear whether it extends similarly to large language models. |
| Approach: | They propose a prompting approach that embeds semantic hints within the prompt to improve LLM performance. |
| Outcome: | The proposed approach improves LLMs’ performance across various tasks, highlighting the potential of integrating semantic information to improve LLM capabilities. |
Graph Neural Network Enhanced Retrieval for Question Answering of Large Language Models (2025.naacl-long)
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| Challenge: | Existing retrieval methods divide reference documents into passages, treating them in isolation. Existing methods only use contiguous passages or keywords. |
| Approach: | They propose a retrieval method that leverages graph neural networks to exploit relatedness between passages to enhance retrieval. |
| Outcome: | The proposed method improves retrieval by exploiting the relatedness between passages. |
Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have achieved impressive performance across diverse tasks, but suffer from the "reversal curse" this limitation poses a challenge to the advancement of artificial general intelligence (AGI) |
| Approach: | They propose to use training data to permute training sentences into entities and feed them into the model. |
| Outcome: | The proposed method improves the performance of large language models (LLMs) on reversed questions and improves existing models. |
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction (2023.acl-long)
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| Challenge: | Existing studies predict sentiment elements in a fixed order, which ignores the interdependence of the elements and the diversity of language expression. |
| Approach: | They propose a multi-view process that aggregates sentiment elements generated in different order . they use element order prompts to guide the language model to generate multiple tuples with different element order based on a given text . |
| Outcome: | The proposed method outperforms existing methods on 10 datasets of 4 benchmark tasks and is highly flexible and transferable across tasks. |