Papers by Delong Chen
What Makes for Good Image Captions? (2025.findings-emnlp)
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| Challenge: | a formal information-theoretic framework is developed for image captioning . the pyramid of captions is a method that generates enriched captions by integrating local and global visual information. |
| Approach: | They propose a formal information-theoretic framework for image captioning . they propose 'Pyramid of Captions' method that generates enriched captions . |
| Outcome: | The proposed framework provides a flexible foundation for analyzing and optimizing image captioning systems across diverse task requirements. |
Chain-of-Talkers (CoTalk): Fast Human Annotation of Dense Image Captions (2025.emnlp-main)
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| Challenge: | Existing approaches for optimizing human annotation efforts are limited . et al., 2015) suggest that densely annotated image captions improve vision-language alignment . |
| Approach: | They propose an AI-in-the-loop methodology to maximize the number of annotated samples and improve their comprehensiveness under fixed budget constraints. |
| Outcome: | The proposed method improves annotation speed and retrieval performance over the parallel method. |
Measuring Political Bias in Large Language Models: What Is Said and How It Is Said (2024.acl-long)
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| Challenge: | Existing benchmarks and measures focus on gender and racial biases, but political bias exists in LLMs and can lead to polarization and other harms in downstream applications. |
| Approach: | They propose to analyze the content and style of LLMs generated by political issues and propose a framework that can be scalable to other topics. |
| Outcome: | The proposed framework is easily scalable to other topics and is explainable. |
High-Dimension Human Value Representation in Large Language Models (2025.naacl-long)
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Samuel Cahyawijaya, Delong Chen, Yejin Bang, Leila Khalatbari, Bryan Wilie, Ziwei Ji, Etsuko Ishii, Pascale Fung
| Challenge: | Existing approaches to align large language models with human values and preferences are not able to be applied to all tasks and fields. |
| Approach: | They propose a high-dimensional representation of symbolic human value distributions in LLMs that is orthogonal to model architecture and training data. |
| Outcome: | The proposed representations are evaluated on 15 open-source and commercial LLMs and are self-supervised from the value-relevant output of 8 LLM models. |
Linguistic Minimal Pairs Elicit Linguistic Similarity in Large Language Models (2025.coling-main)
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| Challenge: | a new analysis leverages linguistic minimal pairs to probe the internal linguistic representations of Large Language Models (LLMs). |
| Approach: | They propose to use linguistic minimal pairs to probe the internal linguistic representations of Large Language Models (LLMs). |
| Outcome: | The proposed analysis reveals that linguistic similarity is significantly influenced by training data exposure, leading to higher cross-LLM agreement in higher-resource languages. |