Papers by Seungyeon Jwa
Becoming Experienced Judges: Selective Test-Time Learning for Evaluators (2026.eacl-short)
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| Challenge: | Large language models and visionlanguage models are increasingly used as automatic evaluators. |
| Approach: | They propose a framework that allows evaluators to improve *sequentially* at inference time without additional training or external signals. |
| Outcome: | The proposed framework outperforms strong baselines in two pairwise comparisons. |
OffsetBias: Leveraging Debiased Data for Tuning Evaluators (2024.findings-emnlp)
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| Challenge: | Existing studies have identified that judge models are vulnerable to certain biases, such as favoring longer answers regardless of content. |
| Approach: | They propose to use open-source LLMs to fine-tune judge models and propose a meta-evaluation collection of test cases for each bias type. |
| Outcome: | The proposed meta-evaluation dataset shows that fine-tuning improves the robustness of judge models against biases and improves performance across most evaluation scenarios. |