Sense Representations Are Inducible Interfaces
📰 ArXiv cs.AI
Learn how to induce sense representations into pre-trained language models for improved disambiguation and cross-lingual alignment
Action Steps
- Implement the ACROS method to induce sense representations into a pre-trained decoder LM
- Use a gated residual addition to integrate the sense pathway into the model
- Evaluate the performance of the modified model on tasks such as disambiguation and cross-lingual alignment
- Fine-tune the model to optimize its quality and sense representation capabilities
- Apply the induced sense representations to real-world applications such as language translation and text summarization
Who Needs to Know This
NLP researchers and engineers can benefit from this technique to enhance their language models' capabilities, while software engineers can apply this to improve the accuracy of their AI-powered applications
Key Insight
💡 Sense representations can be induced into pre-trained language models without requiring pre-training with sense structure
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🤖 Induce sense representations into pre-trained LMs for improved NLP tasks! 📚
Key Takeaways
Learn how to induce sense representations into pre-trained language models for improved disambiguation and cross-lingual alignment
Full Article
Title: Sense Representations Are Inducible Interfaces
Abstract:
arXiv:2605.28669v1 Announce Type: cross Abstract: Sense representations (explicit, per-token meaning decompositions) are useful for disambiguation, steering, and cross-lingual alignment, but existing approaches require models to be pretrained with sense structure baked in. We introduce ACROS, which induces an explicit sense pathway into a frozen pretrained decoder LM through a gated residual addition. On SmolLM2-360M, ACROS preserves base LM quality while supporting three uses of the same induce
Abstract:
arXiv:2605.28669v1 Announce Type: cross Abstract: Sense representations (explicit, per-token meaning decompositions) are useful for disambiguation, steering, and cross-lingual alignment, but existing approaches require models to be pretrained with sense structure baked in. We introduce ACROS, which induces an explicit sense pathway into a frozen pretrained decoder LM through a gated residual addition. On SmolLM2-360M, ACROS preserves base LM quality while supporting three uses of the same induce
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