Papers by Isao Echizen
EASE: Entity-Aware Contrastive Learning of Sentence Embedding (2022.naacl-main)
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| Challenge: | Existing methods for learning sentence embeddings are fine-tuning general-purpose pretrained models with a particular training supervision. |
| Approach: | They propose a method for learning sentence embeddings via contrastive learning between sentences and related entities. |
| Outcome: | The proposed method outperforms baseline methods in multilingual settings on a variety of tasks. |
Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly used as automated evaluators . et al., 2024: strong labels can foster trust but also undermine it . |
| Approach: | They show that LLMs' source labels bias trust judgments by humans . they use eye-tracking data to analyze LLM internal states during judgment . |
| Outcome: | The proposed model is biased by disclosed source labels, the authors show . eye-tracking data show humans rely heavily on source labels for judgments . |
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of Transformations (2023.findings-acl)
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| Challenge: | Existing methods for detecting adversarial attacks need original/adversarial data. |
| Approach: | They propose to detect adversarial text by voting on hard labels from predictions of transformations using a model that uses original/adversarial data. |
| Outcome: | The proposed method detects adversarial text across various state-of-the-art attacks, models, and datasets without training. |