To Copy or Not to Copy: Copying Is Easier to Induce Than Recall

Mehrdad Farahani, Franziska Penzkofer, Richard Johansson


Abstract
Language models used in retrieval-augmented settings choose between parametric knowledge stored in their weights and contextual information in the prompt. This work presents a mechanistic study of that choice: using an approach based on steering vectors, we carry out interventions at selected layers and token spans, and we are able to toggle the model’s behavior in two directions: Copy→Recall (suppressing context use) and Recall→Copy (inducing the model to copy any token from the context). Experiments on three architectures (decoder-only and encoder/decoder) and two open-domain QA benchmarks show consistent behavior shifts under moderate scaling while monitoring accuracy and fluency. Mechanistic analyses of attention routing, MLP contributions, and layer-wise probability trajectories reveal an asymmetry: inducing copying is an easy "reactivation" process that can be triggered at different locations in the input, while restoring recall is a "suppression" process that is more fragile and strongly tied to object-token interventions.
Anthology ID:
2026.findings-emnlp.2329
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2026
Month:
October
Year:
2026
Address:
Budapest, Hungary
Editors:
Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
50027–50090
Language:
URL:
https://aclanthology.org/2026.findings-emnlp.2329/
DOI:
10.18653/2026.findings-emnlp.2329
Bibkey:
Cite (ACL):
Mehrdad Farahani, Franziska Penzkofer, and Richard Johansson. 2026. To Copy or Not to Copy: Copying Is Easier to Induce Than Recall. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 50027–50090, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):
To Copy or Not to Copy: Copying Is Easier to Induce Than Recall (Farahani et al., Findings 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.findings-emnlp.2329.pdf