What if LLMs Ate Their Words: Causal History Effects in Multi-Turn Interaction
Multi-turn interaction creates a feedback process in which an LLM's previous responses become context for later behavior. Prior work shows substantial multi-turn degradation and that assistant-generated history can affect later behavior. However, it remains unclear how these effects manifest across models, tasks, turns, and inside a model. We study these gaps across six task families and five models. Degradation from fully specified single-turn input (FULL) to progressively revealed multi-turn interaction (SHARDED) is clearly task- and model-dependent, and stronger one-shot performance does not imply greater interaction robustness. We then retrospectively analyze completed SHARDED convers...
| Authors | Jinnan Li, Zheren Fu, Yue Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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