Researchers prove Prefix-Tuning can exactly steer pretrained transformers
A new arXiv paper titled “Prefix‑Tuning for Arbitrary Output Sequences on Pretrained Transformers” provides a theoretical proof that a task‑specific prefix can be constructed to make a pretrained autoregressive transformer generate any desired output sequence, as long as that output lies within the model’s reachable range. The authors show the required prefix can be derived by solving multiple…
Key points
- Proof that a prefix exists for any (x, y) pair if y is within the pretrained transformer's output range.
- Prefixes can be explicitly computed by solving multiple linear systems, providing a constructive algorithm.
- Experiments on GPT‑2 and Qwen3‑4B confirm strong output control, but prefixes may not exist when y is out of range.
The authors validate their algorithm with extensive experiments on GPT‑2 and Qwen3‑4B, demonstrating that the computed prefixes exert strong control over the models’ outputs. They also show that when the target output falls outside the model’s range, a suitable prefix may not exist, highlighting the method’s theoretical limits and delivering the first complete answer to the long‑standing question about the expressiveness of prefix representations.
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