Verbalizing Multi-Token Concepts in LLMs
Lens methods inspect model computation by mapping intermediate activations to vocabulary tokens. Yet the concepts humans need to read out often span multiple tokens---entities, phrases, intermediate objects---making token-level readouts incomplete. Reliable multi-token readout with little model-specific preparation remains challenging. We introduce Concept Lens: token-level lens clues guide candidate concept search, then the model derives a representation for each candidate and scores it against the original activation. Across 2,400 multi-hop clozes on five LLMs (8B--70B), Concept Lens instantiated with J-lens and R-lens achieves average Rank@10 scores of 36.6\% and 54.5\%, respectively, ...
| Authors | Xijie Gong, Zimeng Huang, Tonghan Wang |
|---|---|
| Primary category | cs.CL |
| Published | 31 August 2026 |
| Last updated at source | 28 September 2026 |
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