Papers by Yejin Bang
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. |
Mitigating Framing Bias with Polarity Minimization Loss (2023.findings-emnlp)
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| Challenge: | polarity is a pervasive problem in modern media, misleading the understanding of what really happened via a skewed selection of information and language. |
| Approach: | They propose a loss function that encourages the model to minimize the polarity difference between the skewed input articles to reduce framing bias. |
| Outcome: | The proposed loss improves the model's ability to map polarity ends bidirectionally. |
Towards Few-shot Fact-Checking via Perplexity (2021.naacl-main)
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| Challenge: | Recent studies have shown that pre-trained language models can perform few-shot learning for various downstream tasks, such as question answering and machine translation. |
| Approach: | They propose a method to leverage the powerful transfer learning ability of a language model via a perplexity score to learn few-shot for the fact-checking task. |
| Outcome: | The proposed method outperforms the Major Class baseline by 10% on the F1-Macro metric across multiple datasets. |
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. |
HalluLens: LLM Hallucination Benchmark (2025.acl-long)
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Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, Pascale Fung
| Challenge: | Large language models (LLMs) generate responses that deviate from user input or training data, a phenomenon known as "hallucination" . |
| Approach: | They propose a hallucination benchmark HalluLens that includes both extrinsic and intrinsic evaluation tasks to distinguish between extrindic and intrinsic hallucines. |
| Outcome: | The proposed framework disentangles LLM hallucination from "factuality" and distinguishes between extrinsic and intrinsic hallucines to promote consistency and facilitate research. |
NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias (2022.naacl-main)
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| Challenge: | a new task is proposed to reduce media news framing bias by generating a neutral summary from multiple news articles of the varying political leanings. |
| Approach: | They propose a task that generates a neutral summary from multiple news articles . they find title provides a good signal for framing bias and propose metric and model . |
| Outcome: | The proposed task can neutralize news content in hierarchical order from title to article . scalability remains a bottleneck due to the time-consuming human labor needed for composing the roundup . |
Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations (2025.emnlp-main)
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Ziwei Ji, Lei Yu, Yeskendir Koishekenov, Yejin Bang, Anthony Hartshorn, Alan Schelten, Cheng Zhang, Pascale Fung, Nicola Cancedda
| Challenge: | LLMs often use assertive language when making false claims, resulting in harm and loss of trust. |
| Approach: | They find that a mismatch between semantic and verbal uncertainty is a better predictor of hallucinations than semantic uncertainty alone. |
| Outcome: | a new study shows that mismatch between semantic and verbal uncertainty is better predictor of hallucinations than semantic uncertainty alone. |