Papers by Inha Cha
Culture is Everywhere: A Call for Intentionally Cultural Evaluation (2025.findings-emnlp)
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| Challenge: | Existing approaches to evaluate cultural alignment of large language models are too trivial and focus on static facts and values. |
| Approach: | They argue for intentionally cultural evaluation: an approach that examines cultural assumptions . they characterize what, how, and circumstances by which culturally contingent considerations arise in evaluation . |
| Outcome: | The authors argue for intentionally cultural evaluation: an approach that examines cultural assumptions embedded in all aspects of evaluation, not just in explicitly cultural tasks. |
Uncovering Factor-Level Preference to Improve Human-Model Alignment (2025.findings-emnlp)
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| Challenge: | Large language models exhibit tendencies that diverge from human preferences, such as favoring certain writing styles or producing overly verbose outputs. |
| Approach: | They propose a framework to uncover and measure factor-level preference alignment of humans and large language models (LLMs) |
| Outcome: | The proposed framework uncovers and measures factor-level preference alignment of humans and large language models. |
The Generative AI Paradox in Evaluation: “What It Can Solve, It May Not Evaluate” (2024.eacl-srw)
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| Challenge: | Existing studies on using Large Language Models for model evaluation have focused on using LLMs for reference-free evaluation to meet the needs of long-form text evaluation. |
| Approach: | They propose to use Large Language Models (LLMs) for generation tasks to evaluate models. |
| Outcome: | The proposed model evaluations show that LLMs are less faithful to evaluation tasks than open-source models. |