Search Results for author: Xudong Hong

Found 9 papers, 2 papers with code

Do large language models and humans have similar behaviors in causal inference with script knowledge?

1 code implementation13 Nov 2023 Xudong Hong, Margarita Ryzhova, Daniel Adrian Biondi, Vera Demberg

However, reading times remain similar when cause A is not explicitly mentioned, indicating that humans can easily infer event B from their script knowledge.

Causal Inference

HowToCaption: Prompting LLMs to Transform Video Annotations at Scale

1 code implementation7 Oct 2023 Nina Shvetsova, Anna Kukleva, Xudong Hong, Christian Rupprecht, Bernt Schiele, Hilde Kuehne

Specifically, we prompt an LLM to create plausible video descriptions based on ASR narrations of the video for a large-scale instructional video dataset.

Automatic Speech Recognition Sentence +3

Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences

no code implementations20 Jan 2023 Xudong Hong, Asad Sayeed, Khushboo Mehra, Vera Demberg, Bernt Schiele

The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence.

Coherence Evaluation Grounded language learning +3

Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis

no code implementations27 Jan 2021 Yichao Du, Pengfei Luo, Xudong Hong, Tong Xu, Zhe Zhang, Chao Ren, Yi Zheng, Enhong Chen

Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making.

Decision Making

Diverse and Relevant Visual Storytelling with Scene Graph Embeddings

no code implementations CONLL 2020 Xudong Hong, Rakshith Shetty, Asad Sayeed, Khushboo Mehra, Vera Demberg, Bernt Schiele

A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text.

Visual Storytelling

Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders

no code implementations WS 2019 Xudong Hong, Ernie Chang, Vera Demberg

The Multilingual Surface Realization Shared Task 2019 focuses on generating sentences from lemmatized sets of universal dependency parses with rich features.

Data Augmentation Text Generation

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