Your Transformer Can Hold Two Thoughts at Once: Evidence of Linear Superposition in LLMs

TL;DR - This paper presents evidence that Transformers can linearly superpose distinct text streams, producing a combined next-token distribution resembling the average of their individual distributions. Lightweight fine-tuning and guided decoding restore and exploit this behavior to generate two coherent continuations from one forward pass.

• The authors call this behavior the Superposition Linearity Hypothesis.
• Experiments suggest linear superposition is intrinsic to Transformer architecture but weakens during pretraining.
• Lightweight fine-tuning substantially reduces divergence between superposed predictions and averaged individual predictions.
• A guided decoding method disentangles the combined output into two simultaneous, coherent generations.