Cosine Similarity as Logits?: A Scalable Knowledge Probe Using Embedding Vectors from Generative Language Models
Tomoyuki Jinno, Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
- Anthology ID:
- 2026.eacl-long.382
- Volume:
- Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- March
- Year:
- 2026
- Address:
- Rabat, Morocco
- Editors:
- Vera Demberg, Kentaro Inui, Lluís Marquez
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8188–8200
- Language:
- URL:
- https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.382/
- DOI:
- Cite (ACL):
- Tomoyuki Jinno, Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito, and Taro Watanabe. 2026. Cosine Similarity as Logits?: A Scalable Knowledge Probe Using Embedding Vectors from Generative Language Models. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8188–8200, Rabat, Morocco. Association for Computational Linguistics.
- Cite (Informal):
- Cosine Similarity as Logits?: A Scalable Knowledge Probe Using Embedding Vectors from Generative Language Models (Jinno et al., EACL 2026)
- PDF:
- https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.382.pdf