Papers by Xudong Hong
Do large language models and humans have similar behaviours in causal inference with script knowledge? (2024.starsem-1)
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| Challenge: | Recent studies show pre-trained language models have superior language understanding abilities, including zero-shot causal reasoning. |
| Approach: | They used a script-based story to manipulate event B in a story which causally depends on a previous event A. |
| Outcome: | The results show that only recent LLMs, like GPT-3 or Vicuna, correlate with human behavior in the A B condition. |
Retrieval-Augmented Modular Prompt Tuning for Low-Resource Data-to-Text Generation (2024.lrec-main)
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| Challenge: | Data-to-text generation methods are often limited by data sparsity and lack of training data. |
| Approach: | They propose a retrieval-augmented modular prompt tuning method that generates texts with few hallucinations from structured data inputs. |
| Outcome: | The proposed method generates texts with few hallucinations and achieves state-of-the-art performance on a dataset for drone handover message generation. |
Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders (D19-63)
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| Challenge: | The goal of the multilingual surface realization shared task is to generate fluent text from UD structures. |
| Approach: | They propose to use a graph convolutional network to encode the dependency trees given as input. |
| Outcome: | The proposed system achieves the third rank without data augmentation techniques or additional components. |
Visual Coherence Loss for Coherent and Visually Grounded Story Generation (2023.findings-acl)
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| Challenge: | Existing visual storytelling models fail to generate correct referring expressions for characters, causing 60% of the generated stories to be lacking local coherence. |
| Approach: | They propose a loss function inspired by a linguistic theory of coherence for self-supervised learning for image sequence representations and a feature matching metric to check whether the models generate referring expressions correctly for characters in input image sequences. |
| Outcome: | The proposed features and loss function are effective for generating more coherent and visually grounded stories. |
Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences (2023.tacl-1)
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| Challenge: | Existing work on image-based story generation lacks coherent plots for story generation. |
| Approach: | They propose to use image sequences to generate stories from a dataset that has more coherent plots. |
| Outcome: | The proposed model produces more coherent, visually grounded and diverse stories than existing models. |