Beyond the Assistant Turn: User Turn Generation as a Probe of Interaction Awareness in Language Models
Standard LLM benchmarks evaluate the assistant turn: the model generates a response to an input, a verifier scores correctness, and the analysis ends. This paradigm leaves unmeasured whether the LLM encodes any awareness of what follows the assistant response. We propose user-turn generation as a probe of this gap: given a conversation context of user query and assistant response, we let a model generate under the user role. If the model's weights encode interaction awareness, the generated user turn will be a grounded follow-up that reacts to the preceding context. Through experiments across $11$ open-weight LLMs (Qwen3.5, gpt-oss, GLM) and $5$ datasets (math reasoning, instruction follo...
| Authors | Sarath Shekkizhar, Romain Cosentino, Adam Earle |
|---|---|
| Primary category | cs.AI |
| Published | 2 April 2026 |
| Last updated at source | 3 April 2026 |
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